Determining heart rate with reflected light data

ABSTRACT

A physiological monitor switches between a time domain analysis and a frequency domain analysis for determining a heart rate based on objective indicia of signal quality in an acquired heart rate signal.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.14/289,330 filed on May 28, 2014, which is a continuation of U.S. patentapplication Ser. No. 14/198,437 filed on Mar. 5, 2014 (now U.S. Pat. No.11,185,241), where the entire content of each of the foregoing is herebyincorporated by reference.

This application is also related to U.S. Provisional Patent ApplicationNo. 61/696,525, filed Sep. 4, 2012, U.S. Provisional Patent ApplicationNo. 61/736,310, filed Dec. 12, 2012, U.S. patent application Ser. No.14/018,262, filed Sep. 4, 2013, and International Application No.PCT/US2013/058077, filed Sep. 4, 2013. The entire contents of each ofthe aforementioned applications are incorporated herein in theirentirety by reference.

BACKGROUND

There is an increasing demand for health and fitness monitors andmethods for providing health and fitness monitoring. Monitoring heartrate, for example, is important for various reasons. Monitoring heartrate is critical for athletes in understanding their fitness levels andworkouts over time. Conventional techniques for monitoring heart ratehave numerous drawbacks. Certain conventional heart rate monitors, forexample, require the use of a chest strap or other bulky equipment thatcauses discomfort and prevents continuous wearing and use. This presentsa challenge to adoption and use of such monitors because the monitorsare too obtrusive and/or are directed to assessing general well-beingrather than continuous, around-the-clock monitoring of fitness. Certainconventional heart rate monitors do not enable continuous sensing ofheart rate, thereby preventing continuous fitness monitoring andreliable analysis of physiological data. Additionally, a challenge toadoption of fitness monitors by athletes is the lack of a vibrant andinteractive online community for displaying and sharing physiologicaldata among users.

There remains a need for improved continuous heart rate monitoring andinterpretation.

SUMMARY

Disclosed herein is a device for continuous physiological monitoring aswell as systems and methods for interpreting data from such a device.The systems and methods may include automatically detecting, assessing,and analyzing exercise activity, physical recovery states, sleep states,and the like. The acquisition of continuous physiological data mayfacilitate automated recommendations concerning changes to sleep,recovery time, exercise routines, and the like.

In general, embodiments may provide physiological measurement systems,devices, and methods for continuous health and fitness monitoring. Alightweight wearable system with a strap may collect variousphysiological data continuously from a wearer without the need foradditional sensing devices. The systems may also enable monitoring ofone or more physiological parameters in addition to heart rateincluding, but not limited to, body temperature, heart rate variability,motion, sleep, stress, fitness level, recovery level, effect of aworkout routine on health, caloric expenditure, and the like.Embodiments may also include computer-executable instructions that, whenexecuted, enable automatic interpretation of one or more physiologicalparameters to assess the cardiovascular intensity experienced by a user(embodied in an intensity score or indicator) and the user's recoveryafter physical exertion (embodied in a recovery score). These indicatorsor scores may be displayed to assist a user in managing the user'shealth and exercise regimen.

In one aspect, a device includes a strap shaped and sized to fit aboutan appendage, a heart rate monitoring system coupled to the strap andconfigured to provide two or more different modes for detecting a heartrate of a wearer of the strap, a sensor coupled to the strap, a memory,and a processor coupled to the strap. The processor may be configured tosense a condition based on a signal from the sensor and to select one ofthe two or more different modes for detecting the heart rate based onthe condition. The processor may be further configured to operate theheart rate monitoring system to obtain continuous heart rate data usingone of the two or more different modes and to store the continuous heartrate data in the memory.

Implementations may have one or more of the following features. Thecondition may be an accuracy of heart rate detection determined using astatistical analysis to provide a confidence level in the accuracy. Theprocessor may be configured to select a different one of the modes whenthe confidence level is below a predetermined threshold. The differentone of the modes may employ a frequency domain technique. The conditionmay include a power consumption, a battery charge level, a useractivity, or the like. The user activity may include one or more ofexercise, rest, and sleep. The condition may include a location of thesensor or a motion of the sensor. The different modes may include atleast one mode using light emitted from a light source on the strap anddetected by an optical detector on the strap. The at least one mode mayemploy a peak detection technique applied to signals from the opticaldetector. The at least one mode may employ a frequency domain techniqueapplied to signals from the optical detector. The different modes mayinclude one or more modes using variable optical characteristics of thelight source. The variable optical characteristics may include at leastone of a brightness of the light source, a duty cycle of the lightsource, and a color of the light source. The different modes may includeat least one non-optical mode. The sensor may include one or more of amotion sensor, a position sensor, a timer, a temperature sensor, aelectrodermal activity (EDA) sensor (also referred to as a Galvanic SkinResponse (GSR) sensor), and a humidity sensor.

In another aspect, a method includes providing a strap shaped and sizedto fit about an appendage, where the strap includes a sensor and a heartrate monitoring system configured to provide two or more different modesfor detecting a heart rate of a wearer of the strap. The method mayfurther include detecting a signal from the sensor, determining acondition of the heart rate monitoring system based upon the signal,selecting one of the two or more different modes for detecting the heartrate based on the condition, and storing continuous heart rate datausing the one of the two or more different modes.

Implementations may have one or more of the following features. Themethod may include communicating the continuous heart rate data from thestrap to a remote data repository. The method may include detecting achange in the condition, responsively selecting a different one of thetwo or more different modes, and storing additional continuous heartrate data obtained using the different one of the two or more differentmodes.

In yet another aspect, a computer program product for operating awearable physical monitoring system including a sensor and a heart ratemonitoring system configured to provide two or more different modes fordetecting a heart rate of a wearer of the wearable physical monitoringsystem, the computer program product including non-transitory computerexecutable code embodied in a computer readable medium that, whenexecuting on the wearable physical monitoring system, performs the stepsof: detecting a signal from the sensor; determining a condition of theheart rate monitoring system based upon the signal; selecting one of thetwo or more different modes for detecting the heart rate based on thecondition; and storing continuous heart rate data using the one of thetwo or more different modes.

In another aspect, a device includes a wearable strap configured to becouplable to an appendage of a user, one or more light emitters foremitting light toward the user's skin, one or more light detectors forreceiving light reflected from the user's skin, and a processorconfigured to analyze data corresponding to the reflected light toautomatically and continually determine a heart rate of the user,thereby providing continuous heart rate data. The device may furtherinclude a communication system configured to transmit the continuousheart rate data to a remote data repository, and a privacy switchoperable by the user to controllably restrict communication of a portionof the continuous heart rate data to the remote data repository.

Implementations may have one or more of the following features. Theprivacy switch may include a shared setting where continuous heart ratedata is available to a shared data repository. The shared datarepository may be maintained by an administrator for a sports team. Theshared data repository may be maintained on a social networking websiteavailable to one or more members of a social network of the user. Theprivacy switch may include a private setting where continuous heart ratedata is not shared by the user. Continuous heart rate data may be storedlocally for private use by the user when in the private setting.Continuous heart rate data may not be saved when in the private setting.The privacy switch may toggle between a private setting and a sharedsetting. The device may include a display with an indicator of a currentprivacy setting of the privacy switch. The privacy switch may be locatedon the strap of the device. The privacy switch may be located on a localcomputing device associated with the user. The local computing devicemay include a mobile computing device. The mobile computing device mayinclude one or more of a laptop, a tablet, and a smart phone. Theprivacy switch may be hosted on a website accessible to the user througha web page. The device may also include a schedule configured toautomatically change a setting of the privacy switch on a predeterminedschedule. The privacy switch may provide three or more differentuser-selectable privacy settings. The continuous heart rate data mayinclude summary data for a continuous heart rate of the user. Theprivacy switch may be operable by the user to controllably restrictcommunication of other fitness data obtained by the device. The otherfitness data may include an activity of the user, where the activityselected from a group consisting of exercising, resting, and sleeping.The device may include one or more sensors, where the other fitness dataincludes data from the one or more sensors.

In yet another aspect, a method includes: monitoring data from awearable, continuous-monitoring, physiological measurement system wornby a user; automatically detecting exercise activity of the user;generating a quantitative assessment of the exercise activity;automatically detecting a physical recovery state of the user;generating a quantitative assessment of the physical recovery state; andanalyzing the quantitative assessment of the exercise activity and thequantitative assessment of the physical recovery to automaticallygenerate a recommendation on a change to an exercise routine of theuser.

Implementations may have one or more of the following features.Generating a quantitative assessment of the exercise activity mayinclude analyzing the exercise activity on a remote server. The methodmay further include determining a qualitative assessment of the exerciseactivity and communicating the qualitative assessment to the user.Generating a quantitative assessment of the physical recovery state mayinclude analyzing the physical recovery state on a remote server. Themethod may further include determining a qualitative assessment of thephysical recovery state and communicating the qualitative assessment tothe user. The method may further include generating periodic updates tothe user concerning the physical recover state. The recommendation maybe generated on a remote server. The method may further includecommunicating the recommendation to the user in an electronic mail. Themethod may further include presenting the recommendation to the user ina web page. The method may further include generating the recommendationbased upon a number of cycles of exercise and rest.

In another aspect, a computer program product including non-transitorycomputer executable code embodied in a non-transitory computer-readablemedium that, when executing on one or more computing devices, performsthe steps of: monitoring data from a wearable, continuous-monitoring,physiological measurement system worn by a user; automatically detectingexercise activity of the user; generating a quantitative assessment ofthe exercise activity; automatically detecting a physical recovery stateof the user; generating a quantitative assessment of the physicalrecovery state; and analyzing the quantitative assessment of theexercise activity and the quantitative assessment of the physicalrecovery to automatically generate a recommendation on a change to anexercise routine of the user.

Implementations may have one or more of the following features.Generating a quantitative assessment of the exercise activity mayinclude analyzing the exercise activity on a remote server. The computerprogram product may further include code that performs the step ofdetermining a qualitative assessment of the exercise activity andcommunicating the qualitative assessment to the user. Generating aquantitative assessment of the physical recovery state may includeanalyzing the physical recovery state on a remote server. The computerprogram product may further include code that performs the steps ofdetermining a qualitative assessment of the physical recovery state andcommunicating the qualitative assessment to the user. The computerprogram product may further include code that performs the step ofgenerating periodic updates to the user concerning the physical recoverstate. The computer program product may further include code thatperforms the step of communicating the recommendation to the user in anelectronic mail. The computer program product may further include codethat performs the step of presenting the recommendation to the user in aweb page. The computer program product may further include code thatperforms the step of generating the recommendation based upon a numberof cycles of exercise and rest.

In yet another aspect, a system includes a memory configured to storedata received from a wearable, continuous-monitoring, physiologicalmeasurement system worn by a user. The system may further include aserver configured to automatically detect exercise activity of the user,generate a quantitative assessment of the exercise activity,automatically detect a physical recovery state of the user, generate aquantitative assessment of the physical recovery state, and analyze thequantitative assessment of the exercise activity and the quantitativeassessment of the physical recovery to automatically generate arecommendation on a change to an exercise routine of the user.Additionally, the system may include a communications interfaceconfigured to transmit the recommendation from the server to the user.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other objects, features, and advantages of thedevices, systems, and methods described herein will be apparent from thefollowing description of particular embodiments thereof, as illustratedin the accompanying figures. The figures are not necessarily to scale,emphasis instead being placed upon illustrating the principles of thedevices, systems, and methods described herein.

FIG. 1 illustrates front and back perspective views of a wearable systemconfigured as a bracelet including one or more straps.

FIGS. 2-4 illustrate various embodiments of a bracelet.

FIG. 5 illustrates placement of a wearable physiological measurementsystem on a user's wrist.

FIG. 6 shows a block diagram illustrating components of a wearablephysiological measurement system configured to provide continuouscollection and monitoring of physiological data.

FIG. 7A illustrates a side view of a physiological measurement systemincluding a strap that is not coupled to a modular head portion.

FIG. 7B illustrates a side view of a physiological measurement system inwhich a modular head portion is removably coupled to the strap.

FIGS. 8A and 8B depict a schematic side view and top view, respectively,of a physiological measurement system including a head portion, a strap,and a multi-chip module.

FIG. 9 is a flowchart illustrating a signal processing algorithm forgenerating a sequence of heart rates for every detected heartbeat thatmay be embodied in computer-executable instructions stored on one ormore non-transitory computer-readable media.

FIG. 10 is a flowchart illustrating a method of determining an intensityscore.

FIG. 11 is a flowchart illustrating a method by which a user may useintensity and recovery scores.

FIG. 12 illustrates a display of an intensity score index indicated in acircular graphic component with an exemplary current score of 19.0indicated.

FIG. 13 illustrates a display of a recovery score index indicated in acircular graphic component with a first threshold of 66% and a secondthreshold of 33% indicated.

FIGS. 14A-14C illustrate the recovery score graphic component withrecovery scores and qualitative information corresponding to therecovery scores.

FIGS. 15A-18B illustrate a user interface for displaying physiologicaldata specific to a user as rendered on visual display device.

FIGS. 19A-19B illustrate a user interface rendered on a visual displaydevice for displaying physiological data on a plurality of users.

FIG. 20 illustrates a user interface that may be used to independentlyselect time periods of data for multiple users so that the data from theselected periods may be displayed together.

FIGS. 21A-21B illustrate a user interface viewable by an administrativeuser, including a selectable and editable listing of users (e.g., atrainer's clients) whose health information is available for display.

FIG. 22 is a block diagram of a computing device that may be usedherein.

FIG. 23 is a block diagram of a distributed computer system in whichvarious aspects and functions in accord with the present disclosure maybe practiced.

FIG. 24 is a diagram of a network environment suitable for a distributedimplementation of embodiments described herein.

FIG. 25 is a flow chart illustrating a method according to animplementation.

FIG. 26 is a flow chart illustrating a method according to animplementation.

DETAILED DESCRIPTION

The embodiments will now be described more fully hereinafter withreference to the accompanying figures, in which preferred embodimentsare shown. The foregoing may, however, be embodied in many differentforms and should not be construed as limited to the illustratedembodiments set forth herein. Rather, these illustrated embodiments areprovided so that this disclosure will convey the scope to those skilledin the art.

All documents mentioned herein are hereby incorporated by reference intheir entirety. References to items in the singular should be understoodto include items in the plural, and vice versa, unless explicitly statedotherwise or clear from the text. Grammatical conjunctions are intendedto express any and all disjunctive and conjunctive combinations ofconjoined clauses, sentences, words, and the like, unless otherwisestated or clear from the context. Thus, the term “or” should generallybe understood to mean “and/or” and so forth.

Recitations of ranges of values herein are not intended to be limiting,referring instead individually to any and all values falling within therange, unless otherwise indicated herein, and each separate value withinsuch a range is incorporated into the specification as if it wereindividually recited herein. The words “about,” “approximately,” or thelike, when accompanying a numerical value, are to be construed asindicating a deviation as would be appreciated by one of ordinary skillin the art to operate satisfactorily for an intended purpose. Ranges ofvalues and/or numeric values are provided herein as examples only, anddo not constitute a limitation on the scope of the describedembodiments. The use of any and all examples, or exemplary language(“e.g.,” “such as,” or the like) provided herein, is intended merely tobetter illuminate the embodiments and does not pose a limitation on thescope of the embodiments. No language in the specification should beconstrued as indicating any unclaimed element as essential to thepractice of the embodiments.

In the following description, it is understood that terms such as“first,” “second,” “above,” “below,” and the like, are words ofconvenience and are not to be construed as limiting terms.

Exemplary embodiments provide physiological measurement systems, devicesand methods for continuous health and fitness monitoring, and provideimprovements to overcome the drawbacks of conventional heart ratemonitors. One aspect of the present disclosure is directed to providinga lightweight wearable system with a strap that collects variousphysiological data or signals from a wearer. The strap may be used toposition the system on an appendage or extremity of a user, for example,wrist, ankle, and the like. Exemplary systems are wearable and enablereal-time and continuous monitoring of heart rate without the need for achest strap or other bulky equipment which could otherwise causediscomfort and prevent continuous wearing and use. The system maydetermine the user's heart rate without the use of electrocardiographyand without the need for a chest strap. Exemplary systems can thereby beused in not only assessing general well-being but also in continuousmonitoring of fitness. Exemplary systems also enable monitoring of oneor more physiological parameters in addition to heart rate including,but not limited to, body temperature, heart rate variability, motion,sleep, stress, fitness level, recovery level, effect of a workoutroutine on health and fitness, caloric expenditure, and the like.

A health or fitness monitor that includes bulky components may hindercontinuous wear. Existing fitness monitors often include thefunctionality of a watch, thereby making the health or fitness monitorquite bulky and inconvenient for continuous wear. Accordingly, oneaspect is directed to providing a wearable health or fitness system thatdoes not include bulky components, thereby making the bracelet slimmer,unobtrusive and appropriate for continuous wear. The ability tocontinuously wear the bracelet further allows continuous collection ofphysiological data, as well as continuous and more reliable health orfitness monitoring. For example, embodiments of the bracelet disclosedherein allow users to monitor data at all times, not just during afitness session. In some embodiments, the wearable system may or may notinclude a display screen for displaying heart rate and otherinformation. In other embodiments, the wearable system may include oneor more light emitting diodes (LEDs) to provide feedback to a user anddisplay heart rate selectively. In some embodiments, the wearable systemmay include a removable or releasable modular head that may provideadditional features and may display additional information. Such amodular head can be releasably installed on the wearable system whenadditional information display is desired, and removed to improve thecomfort and appearance of the wearable system. In other embodiments, thehead may be integrally formed in the wearable system.

Exemplary embodiments also include computer-executable instructionsthat, when executed, enable automatic interpretation of one or morephysiological parameters to assess the cardiovascular intensityexperienced by a user (embodied in an intensity score or indicator) andthe user's recovery after physical exertion or daily stress given sleepand other forms of rest (embodied in a recovery score). These indicatorsor scores may be stored and displayed in a meaningful format to assist auser in managing his health and exercise regimen. Exemplarycomputer-executable instructions may be provided in a cloudimplementation.

Exemplary embodiments also provide a vibrant and interactive onlinecommunity, in the form of a website, for displaying and sharingphysiological data among users. A user of the website may include anindividual whose health or fitness is being monitored, such as anindividual wearing a wearable system disclosed herein, an athlete, asports team member, a personal trainer or a coach. In some embodiments,a user may pick his/her own trainer from a list to comment on theirperformance. Exemplary systems have the ability to stream allphysiological information wirelessly, directly or through a mobilecommunication device application, to an online website using datatransfer to a cell phone/computer. This information, as well as any datadescribed herein, may be encrypted (e.g., the data may include encryptedbiometric data). Thus, the encrypted data may be streamed to a secureserver for processing. In this manner, only authorized users will beable to view the data and any associated scores. In addition, or in thealternative, the website may allow users to monitor their own fitnessresults, share information with their teammates and coaches, competewith other users, and win status. Both the wearable system and thewebsite allow a user to provide feedback regarding his/her day, exerciseand/or sleep, which enables recovery and performance ratings.

In an exemplary technique of data transmission, data collected by awearable system may be transmitted directly to a cloud-based datastorage, from which data may be downloaded for display and analysis on awebsite. In another exemplary technique of data transmission, datacollected by a wearable system may be transmitted via a mobilecommunication device application to a cloud-based data storage, fromwhich data may be downloaded for display and analysis on a website.

In some embodiments, the website may be a social networking site. Insome embodiments, the website may be displayed using a mobile website ora mobile application. In some embodiments, the website may be configuredto communicate data to other websites or applications. In someembodiments, the website may be configured to provide an interactiveuser interface. The website may be configured to display results basedon analysis on physiological data received from one or more devices. Thewebsite may be configured to provide competitive ways to compare oneuser to another, and ultimately a more interactive experience for theuser. For example, in some embodiments, instead of merely comparing auser's physiological data and performance relative to that user's pastperformances, the user may be allowed to compete with other users andthe user's performance may be compared to that of other users.

I. DEFINITIONS OF TERMS

Certain terms are defined below to facilitate understanding of exemplaryembodiments.

The term “user” as used herein, refers to any type of animal, human ornon-human, whose physiological information may be monitored using anexemplary wearable physiological monitoring system.

The term “body,” as used herein, refers to the body of a user.

The term “continuous,” as used herein in connection with heart rate datacollection, refers to collection of heart rate data at a sufficientfrequency to enable detection of every heart beat and also refers tocollection of heart rate data continuously throughout the day and night.

The term “pointing device,” as used herein, refers to any suitable inputinterface, specifically, a human interface device, that allows a user toinput spatial data to a computing system or device. In an exemplaryembodiment, the pointing device may allow a user to provide input to thecomputer using physical gestures, for example, pointing, clicking,dragging, and dropping. Exemplary pointing devices may include, but arenot limited to, a mouse, a touchpad, a touchscreen, and the like.

The term “multi-chip module,” as used herein, refers to an electronicpackage in which multiple integrated circuits (IC) are packaged with aunifying substrate, facilitating their use as a single component, i.e.,as a higher processing capacity IC packaged in a much smaller volume.

The term “computer-readable medium,” as used herein, refers to anon-transitory storage hardware, non-transitory storage device ornon-transitory computer system memory that may be accessed by acontroller, a microcontroller, a computational system or a module of acomputational system to encode thereon computer-executable instructionsor software programs. The “computer-readable medium” may be accessed bya computational system or a module of a computational system to retrieveand/or execute the computer-executable instructions or software programsencoded on the medium. The non-transitory computer-readable media mayinclude, but are not limited to, one or more types of hardware memory,non-transitory tangible media (for example, one or more magnetic storagedisks, one or more optical disks, one or more USB flash drives),computer system memory or random access memory (such as, DRAM, SRAM, EDORAM) and the like.

The term “distal,” as used herein, refers to a portion, end or componentof a physiological measurement system that is farthest from a user'sbody when worn by the user.

The term “proximal,” as used herein, refers to a portion, end orcomponent of a physiological measurement system that is closest to auser's body when worn by the user.

The term “equal,” as used herein, refers, in a broad lay sense, to exactequality or approximate equality within some tolerance.

II. EXEMPLARY WEARABLE PHYSIOLOGICAL MEASUREMENT SYSTEMS

Exemplary embodiments provide wearable physiological measurementssystems that are configured to provide continuous measurement of heartrate. Exemplary systems are configured to be continuously wearable on anappendage, for example, wrist or ankle, and do not rely onelectrocardiography or chest straps in detection of heart rate. Theexemplary system includes one or more light emitters for emitting lightat one or more desired frequencies toward the user's skin, and one ormore light detectors for received light reflected from the user's skin.The light detectors may include a photo-resistor, a photo-transistor, aphoto-diode, and the like. As light from the light emitters (forexample, green light) pierces through the skin of the user, the blood'snatural absorbance or transmittance for the light provides fluctuationsin the photo-resistor readouts. These waves have the same frequency asthe user's pulse since increased absorbance or transmittance occurs onlywhen the blood flow has increased after a heartbeat. The system includesa processing module implemented in software, hardware or a combinationthereof for processing the optical data received at the light detectorsand continuously determining the heart rate based on the optical data.The optical data may be combined with data from one or more motionsensors, e.g., accelerometers and/or gyroscopes, to minimize oreliminate noise in the heart rate signal caused by motion or otherartifacts (or with other optical data of another wavelength).

FIG. 1 illustrates front and back perspective views of one embodiment ofa wearable system configured as a bracelet 100 including one or morestraps 102. FIGS. 2 and 3 show various exemplary embodiments of abracelet according to aspects disclosed herein. FIG. 4 illustrates anexemplary user interface of a bracelet. The bracelet is sleek andlightweight, thereby making it appropriate for continuous wear. Thebracelet may or may not include a display screen, e.g., a screen 106such as a light emitting diode (LED) display for displaying any desireddata (e.g., instantaneous heart rate), as shown and described below withreference to the exemplary embodiments in FIGS. 2-4 .

As shown in the non-limiting embodiment in FIG. 1 , the strap 102 of thebracelet may have a wider side and a narrower side. In one embodiment, auser may simply insert the narrower side into the thicker side andsqueeze the two together until the strap is tight around the wrist, asshown in FIG. 5 . To remove the strap, a user may push the strap furtherinwards, which unlocks the strap and allows it to be released from thewrist. In other embodiments, various other fastening means may beprovided. For example, the fastening mechanism may include, withoutlimitation, a clasp, clamp, clip, dock, friction fit, hook and loop,latch, lock, pin, screw, slider, snap, button, spring, yoke, and so on.

In some embodiments, the strap of the bracelet may be a slim elasticband formed of any suitable elastic material, for example, rubber.Certain embodiments of the wearable system may be configured to have onesize that fits all. Other embodiments may provide the ability to adjustfor different wrist sizes. In one aspect, a combination of constantmodule strap material, a spring-loaded, floating optical system and asilicon-rubber finish may be used in order to achieve coupling whilemaintaining the strap's comfort for continuous use. Use of medical-gradematerials to avoid skin irritations may be utilized.

As shown in FIG. 1 , the wearable system may include componentsconfigured to provide various functions such as data collection andstreaming functions of the bracelet. In some embodiments, the wearablesystem may include a button underneath the wearable system. In someembodiments, the button may be configured such that, when the wearablesystem is properly tightened to one's wrist as shown in FIG. 3A, thebutton may press down and activate the bracelet to begin storinginformation. In other embodiments, the button may be disposed andconfigured such that it may be pressed manually at the discretion of auser to begin storing information or otherwise to mark the start or endof an activity period. In some embodiments, the button may be held toinitiate a time stamp and held again to end a time stamp, which may betransmitted, directly or through a mobile communication deviceapplication, to a website as a time stamp.

Time stamp information may be used, for example, as a privacy setting toindicate periods of activity during which physiological data may not beshared with other users. In one aspect, the button may be tapped,double-tapped (or triple-tapped or more), or held down in order toperform different functions or display different information (e.g.,display battery information, generate time stamps, etc.). Otherimplementations may include more or less buttons or other forms ofinterfaces. More general, a privacy switch such as any of the userinputs or controls described herein may be operated to controlrestrictions on sharing, distribution, or use of heart rate or othercontinuously monitored physiological data. For example, the privacyswitch may include a toggle switch to switch between a private settingwhere data is either not gathered at all or where data is stored locallyfor a user, and between a public, shared, or other non-private settingwhere data is communicated over a network and/or to a shared datarepository. The privacy switch may also support numerous levels ofprivacy, e.g., using a hierarchical, role-based, and/or identity-basedarrangement of permitted users and/or uses. As another example, variouslevels of privacy may be available for the type and amount of data thatis shared versus private. In general, the privacy switch may be aphysical switch on the wearable system, or a logical switch or the likemaintained on a computer or other local or mobile computing device ofthe user, or on a website or other network-accessible resource where theuser can select and otherwise control privacy settings for monitoredphysiological data.

In some embodiments, the wearable system may be waterproof so that usersnever need to remove it, thereby allowing for continuous wear.

The wearable system may include a heart rate monitor. In one example,the heart rate may be detected from the radial artery, in the exemplarypositioning shown in FIG. 5 . See, Certified Nursing Association,“Regular monitoring of your patient's radial pulse can help you detectchanges in their condition and assist in providing potentiallylife-saving care.” See,http://cnatraininghelp.com/cna-skills/counting-and-recording-a-radial-pulse,the entire contents of which are incorporated herein by reference. Thus,the wearable system may include a pulse sensor. In one embodiment, thewearable system may be configured such that, when a user wears it aroundtheir wrist and tightens it, the sensor portion of the wearable systemis secured over the user's radial artery or other blood vessel. Secureconnection and placement of the pulse sensor over the radial artery orother blood vessel may allow measurement of heart rate and pulse. Itwill be understood that this configuration is provided by way of exampleonly, and that other sensors, sensor positions, and monitoringtechniques may also or instead be employed without departing from thescope of this disclosure.

In some embodiments, the pulse or heart rate may be taken using anoptical sensor coupled with one or more light emitting diodes (LEDs),all directly in contact with the user's wrist. The LEDs are provided ina suitable position from which light can be emitted into the user'sskin. In one example, the LEDs mounted on a side or top surface of acircuit board in the system to prevent heat buildup on the LEDs and toprevent burns on the skin. The circuit board may be designed with theintent of dissipating heat, e.g., by including thick conductive layers,exposed copper, heatsink, or similar. In one aspect, the pulserepetition frequency is such that the amount of power thermallydissipated by the LED is negligible. Cleverly designed elastic wriststraps can ensure that the sensors are always in contact with the skinand that there is a minimal amount of outside light seeping into thesensors. In addition to the elastic wrist strap, the design of the strapmay allow for continuous micro adjustments (no preset sizes) in order toachieve an optimal fit, and a floating sensor module. The sensor modulemay be free to move with the natural movements caused by flexion andextension of the wrist.

In some embodiments, the wearable system may be configured to recordother physiological parameters including, but not limited to, skintemperature (using a thermometer), galvanic skin response (using agalvanic skin response sensor), motion (using one or more multi-axesaccelerometers and/or gyroscope), and the like, and environmental orcontextual parameters, e.g., ambient temperature, humidity, time of day,and the like. In an implementation, sensors are used to provide at leastone of continuous motion detection, environmental temperature sensing,electrodermal activity (EDA) sensing, galvanic skin response (GSR)sensing, and the like. In this manner, an implementation can identifythe cause of a detected physiological event. ReflectancePhotoPlethysmoGraphy (RPPG) may be used for the detection of cardiacactivity, which may provide for non-intrusive data collection, usabilityin wet, dusty and otherwise harsh environments, and low powerrequirements. For example, as explained herein, using the physiologicalreadouts of the device and the analytics described herein, an “IntensityScore” (e.g., 0-21) (e.g., that measures a user's recent exertion), a“Recovery Score” (e.g., 0-100%), and “Sleep Score” (e.g., 0-100) maytogether measure readiness for physical and psychological exertion.

In some embodiments, the wearable system may further be configured suchthat a button underneath the system may be pressed against the user'swrist, thus triggering the system to begin one or more of collectingdata, calculating metrics and communicating the information to anetwork. In some embodiments, the sensor used for, e.g., measuring heartrate or GSR or any combination of these, may be used to indicate whetherthe user is wearing the wearable system or not. In some embodiments,power to the one or more LEDs may be cut off as soon as this situationis detected, and reset once the user has put the wearable system back ontheir wrist.

The wearable system may include one, two or more sources of batterylife, e.g., two or more batteries. In some embodiments, it may have abattery that can slip in and out of the head of the wearable system andcan be recharged using an included accessory. Additionally, the wearablesystem may have a built-in battery that is less powerful. When the morepowerful battery is being charged, the user does not need to remove thewearable system and can still record data (during sleep, for example).

In some embodiments, an application associated with data from anexemplary wearable system (e.g., a mobile communication deviceapplication) may include a user input component for enabling additionalcontextual data, e.g., emotional (e.g., the user's feelings), perceivedintensity, and the like. When the data is uploaded from the wearablesystem directly or indirectly to a website, the website may record auser's “Vibes” alongside their duration of exercise and sleep.

In exemplary embodiments, the wearable system is enabled toautomatically detect when the user is asleep, awake but at rest andexercising based on physiological data collected by the system.

As shown in the exemplary embodiment of FIG. 4 , a rotatable wheel 108may be provided at the center of the wearable system to control whetherthe system is displaying the heart rate. For example, when the wheel isturned to the right however, the system continuously shows heart rate,and turns off the heart rate display when the wheel is turned to theright again. In one example, turning the wheel to the right and holdingit there creates a time stamp to indicate the duration of exercise.Turning the wheel to the left and holding it there forces datatransmission to a cell phone, external computer or the Internet. Inother embodiments, the wheel 108 may be absent in the wearable system.In some embodiments, the functionality of a rotatable wheel describedherein may be provided in an application of a mobile communicationdevice that is associated with physiological data collected from awearable system.

FIG. 6 shows a block diagram illustrating exemplary components of awearable physiological measurement system 600 configured to providecontinuous collection and monitoring of physiological data. The wearablesystem 600 includes one or more sensors 602. As discussed above, thesensors 602 may include a heart rate monitor. In some embodiments, thewearable system 600 may further include one or more of sensors fordetecting calorie burn, distance and activity. Calorie burn may be basedon a user's heart rate, and a calorie burn measurement may be improvedif a user chooses to provide his or her weight and/or other physicalparameters. In some embodiments, manual entering of data is not requiredin order to derive calorie burn; however, data entry may be used toimprove the accuracy of the results. In some embodiments, if a user hasforgotten to enter a new weight, he/she can enter it for past weeks andthe calorie burn may be updated accordingly.

The sensors 602 may include one or more sensors for activitymeasurement. In some embodiments, the system may include one or moremulti-axes accelerometers and/or gyroscope to provide a measurement ofactivity. In some embodiments, the accelerometer may further be used tofilter a signal from the optical sensor for measuring heart rate and toprovide a more accurate measurement of the heart rate. In someembodiments, the wearable system may include a multi-axis accelerometerto measure motion and calculate distance, whether it be in real terms assteps or miles or as a converted number. Activity sensors may be used,for example, to classify or categorize activity, such as walking,running, performing another sport, standing, sitting or lying down. Insome embodiments, one or more of collected physiological data may beaggregated to generate an aggregate activity level. For example, heartrate, calorie burn, and distance may be used to derive an aggregateactivity level. The aggregate level may be compared with or evaluatedrelative to previous recordings of the user's aggregate activity level,as well as the aggregate activity levels of other users.

The sensors 602 may include a thermometer for monitoring the user's bodyor skin temperature. In one embodiment, the sensors may be used torecognize sleep based on a temperature drop, GSR data, lack of activityaccording to data collected by the accelerometer, and reduced heart rateas measured by the heart rate monitor. The body temperature, inconjunction with heart rate monitoring and motion, may be used tointerpret whether a user is sleeping or just resting, as bodytemperature drops significantly when an individual is about to fallasleep), and how well an individual is sleeping as motion indicates alower quality of sleep. The body temperature may also be used todetermine whether the user is exercising and to categorize and/oranalyze activities.

The system 600 includes one or more batteries 604. According to oneembodiment, the one or more batteries may be configured to allowcontinuous wear and usage of the wearable system. In one embodiment, thewearable system may include two or more batteries. The system mayinclude a removable battery that may be recharged using a charger. Inone example, the removable battery may be configured to slip in and outof a head portion of the system, attach onto the bracelet, or the like.In one example, the removable battery may be able to power the systemfor around a week. Additionally, the system may include a built-inbattery. The built-in battery may be recharged by the removable battery.The built-in battery may be configured to power the bracelet for arounda day on its own. When the more removable battery is being charged, theuser does not need to remove the system and may continue collecting datausing the built-in battery. In other embodiments, the two batteries mayboth be removable and rechargeable.

In some embodiments, the system 600 may include a battery that is awireless rechargeable battery. For example, the battery may be rechargedby placing the system or the battery on a rechargeable mat. In otherexample, the battery may be a long range wireless rechargeable battery.In other embodiments, the battery may be a rechargeable via motion. Inyet other embodiments, the battery may be rechargeable using a solarenergy source.

The wearable system 600 includes one or more non-transitorycomputer-readable media 606 for storing raw data detected by the sensorsof the system and processed data calculated by a processing module ofthe system.

The system 600 includes a processor 608, a memory 610, a bus 612, anetwork interface 614 and an interface 616. The network interface 614 isconfigured to wirelessly communicate data to an external network. Someembodiments of the wearable system may be configured to streaminformation wirelessly to a social network. In some embodiments, datastreamed from a user's wearable system to an external network may beaccessed by the user via a website. The network interface may beconfigured such that data collected by the system may be streamedwirelessly. In some embodiments, data may be transmitted automatically,without the need to manually press any buttons. In some embodiments, thesystem may include a cellular chip built into the system. In oneexample, the network interface may be configured to stream data usingBluetooth technology. In another example, the network interface may beconfigured to stream data using a cellular data service, such as via a3G or 4G cellular network.

In some embodiments, a physiological measurement system may beconfigured in a modular design to enable continuous operation of thesystem in monitoring physiological information of a user wearing thesystem. The module design may include a strap and a separate modularhead portion or housing that is removably couplable to the strap. FIG.7A illustrates a side view of an exemplary physiological measurementsystem 100 including a strap 102 that is not coupled to a modular headportion or housing 104. FIG. 7B illustrates a side view of the system100 in which the modular head portion 104 is removably coupled to thestrap 102.

In the non-limiting illustrative module design, the strap 102 of aphysiological measurement system may be provided with a set ofcomponents that enables continuous monitoring of at least a heart rateof the user so that it is independent and fully self-sufficient incontinuously monitoring the heart rate without requiring the modularhead portion 104. In one embodiment, the strap includes a plurality oflight emitters for emitting light toward the user's skin, a plurality oflight detectors for receiving light reflected from the user's skin, anelectronic circuit board comprising a plurality of electronic componentsconfigured for analyzing data corresponding to the reflected light toautomatically and continually determine a heart rate of the user, and afirst set of one or more batteries for supplying electrical power to thelight emitters, the light detectors and the electronic circuit board. Insome embodiments, the strap may also detect one or more otherphysiological characteristics of the user including, but not limited to,temperature, galvanic skin response, and the like. The strap may includeone or more slots for permanently or removably coupling batteries 702 tothe strap 102.

The strap 102 may include an attachment mechanism 706, e.g., a press-fitmechanism, for coupling the modular head portion 104 to the strap 102.The modular head portion 104 may be coupled to the strap 102 at anydesired time by the user to impart additional functionality to thesystem 100. In one embodiment, the modular head portion 104 includes asecond set of one or more batteries 704 chargeable by an external powersource so that the second set of batteries can be used to charge orrecharge the first set of batteries 702 in the strap 102. Thecombination of the first and second sets of batteries enables the userto continuously monitor his/her physiological information without havingto remove the strap for recharging. In some embodiments, the module headportion may include one or more additional components including, but notlimited to, an interface 616 including visual display device configuredto render a user interface for displaying physiological information ofthe user, a GPS sensor, an electronic circuit board (e.g., to processGPS signals), and the like.

Certain exemplary systems may be configured to be coupled to any desiredpart of a user's body so that the system may be moved from one portionof the body (e.g., wrist) to another portion of the body (e.g., ankle)without affecting its function and operation. An exemplary system mayinclude an electronic circuit board comprising a plurality of electroniccomponents configured for analyzing data corresponding to the reflectedlight to automatically and continually determine a heart rate of theuser. The electronic circuit board implements a processing moduleconfigured to detect an identity of a portion of the user's body, forexample, an appendage like wrist, ankle, to which the strap is coupledbased on one or more signals associated with the heart rate of the user,and, based on the identity of the appendage, adjust data analysis of thereflected light to determine the heart rate of the user.

In one embodiment, the identity of the portion of the user's body towhich the wearable system is attached may be determined based on one ormore parameters including, but not limited to, absorbance level of lightas returned from the user's skin, reflectance level of light as returnedfrom the user's skin, motion sensor data (e.g., accelerometer and/orgyroscope), altitude of the wearable system, and the like.

In some embodiments, the processing module is configured to determinethat the wearable system is taken off from the user's body. In oneexample, the processing module may determine that the wearable systemhas been taken off if data from the galvanic skin response sensorindicates data atypical of a user's skin. If the wearable system isdetermined to be taken off from the user's body, the processing moduleis configured to deactivate the light emitters and the light detectorsand cease monitoring of the heart rate of the user to conserve power.

In some exemplary embodiments, the electronic components of thephysiological measurement system may be provided in the form of amulti-chip module in which a plurality of electrically-coupledelectronic circuit boards are provided separately within the system. Inone non-limiting example, the processor and random-access memory (RAM)may be provided on a first circuit board, wireless communicationcomponents may be provided on a second circuit board, and sensors may beprovided on a third circuit board. The separate electronic circuitboards may be provided in a modular head of the system and/or along astrap of the system. The term “multi-chip module,” as used herein,refers to an electronic package in which multiple integrated circuits(IC) are packaged with a unifying substrate, facilitating their use as asingle component, i.e., as a higher processing capacity IC packaged in amuch smaller volume. Each IC can comprise a circuit fabricated in athinned semiconductor wafer. Any suitable set of one or more electroniccomponents may be provided in the circuit boards of a multi-chip module.Exemplary embodiments also provide methods for fabricating andassembling multi-chip modules as taught herein.

Exemplary numbers of chips integrated in a multi-chip module mayinclude, but are not limited to, two, three, four, five, six, seven,eight, and the like. In one embodiment of a physiological measurementsystem, a single multi-chip module is provided on a circuit board thatperforms operations to generate physiological information associatedwith a user of the system. In other embodiments, a plurality ofmulti-chip modules are provided on a circuit board of the physiologicalmeasurement system. The plurality of multi-chip modules may be stackedvertically on top of one another on the circuit board to furtherminimize the packaging size and the footprint of the circuit board.

In one multi-chip embodiment, two or more electrically-coupled circuitboards of a multi-chip module may be provided in a physiologicalmeasurement system in a vertically stacked manner to minimize thepackaging size and the footprint of the circuit board. Verticallystacking the components on a circuit board minimizes the packaging size(e.g., the length and width) and the footprint occupied by the chips onthe circuit board. In certain non-limiting embodiments, a circuit boardincluding one or more physiological sensors may be placed closest to,proximal to or in contact with the user's skin, while one or morecircuit boards including one or more processors, storage devices,communication components and non-physiological sensors may be providedin vertical layers that are distal to the user's skin.

FIGS. 8A and 8B depict a schematic side view and top view, respectively,of an exemplary physiological measurement system 100 including a headportion 104, a strap 102 and a multi-chip module. The head portionand/or the strap may include a circuit board 802 including a multi-chipmodule assembled in a vertically stacked configuration. Two or morelayers of active electronic integrated circuit (IC) components areintegrated vertically into a single circuit in the circuit board. The IClayers are oriented in spaced planes that extend substantially parallelto one another in a vertically stacked configuration. As illustrated inFIG. 8A, the circuit board 802 includes a substrate 804 for supportingthe multi-chip module. A first integrated circuit chip 806 is coupled tothe substrate 804 using any suitable coupling mechanism, for example,epoxy application and curing. A first spacer layer 808 is coupled to thesurface of the first integrated circuit chip 806 opposite to thesubstrate 804 using, for example, epoxy application and curing. A secondintegrated circuit chip 810 is coupled to the surface of the firstspacer layer 808 opposite to the first integrated circuit chip 806using, for example, epoxy application and curing. The first and secondintegrated circuit chips 806 and 810 are electrically coupled usingwiring 812.

In some embodiments, a metal frame may be provided for mechanical and/orelectrical connection among the integrated circuit chips. An exemplarymetal frame may take the form of a lead frame. The first and secondintegrated circuit chips may be coupled to the metal frame using wiring.A packaging may be provided to encapsulate the multi-chip moduleassembly and to maintain the multiple integrated circuit chips insubstantially parallel arrangement with respect to one another.

As illustrated in FIG. 8A, the vertical three-dimensional stacking ofthe first integrated circuit chip 806 and the second integrated circuitchip 810 provides high-density functionality on the circuit board whileminimizing overall packaging size and footprint (as compared to acircuit board that does not employ a vertically stacked multi-chipmodule). One of ordinary skill in the art will recognize that anexemplary multi-chip module is not limited to two stacked integratedcircuit chips. Exemplary numbers of chips vertically integrated in amulti-chip module may include, but are not limited to, two, three, four,five, six, seven, eight, and the like.

In one embodiment, a single multi-chip module is provided. In otherembodiments, a plurality of multi-chip modules as illustrated in FIG. 8Ais provided. In an exemplary embodiment, a plurality of multi-chipmodules (for example, two multi-chip modules) may be stacked verticallyon top of one another on a circuit board of a physiological measurementsystem to further minimize the packaging size and footprint of thecircuit board.

In addition to the need for reducing the footprint, there is also a needfor decreasing the overall package height in multi-chip modules.Exemplary embodiments may employ wafer thinning to sub-hundreds micronto reduce the package height in multi-chip modules. Any suitabletechnique can be used to assemble a multi-chip module on a substrate.Exemplary assembly techniques include, but are not limited to, laminatedMCM (MCM-L) in which the substrate is a multi-layer laminated printedcircuit board, deposited MCM (MCM-D) in which the multi-chip modules aredeposited on the base substrate using thin film technology, and ceramicsubstrate MCM (MCM-C) in which several conductive layers are depositedon a ceramic substrate and embedded in glass layers that layers areco-fired at high temperatures (HTCC) or low temperatures (LTCC).

In another multi-chip embodiment illustrated in FIG. 8B, two or moreelectrically-coupled circuit boards of a multi-chip module may beprovided in a physiological measurement system in a horizontally spacedmanner to minimize the height of the circuit board. Providing thecomponents on a circuit board in a horizontally spaced manner minimizesthe packaging height occupied by the chips on the circuit board. Incertain non-limiting embodiments, a circuit board including one or morephysiological sensors may be placed close to or in contact with theuser's skin so that physiological signals are detected reliably, whileone or more circuit boards including one or more processors, storagedevices, communication components and non-physiological sensors may beprovided may be distributed throughout the wearable system to provideimproved flexibility, wearability, comfort and durability of the system.

FIG. 8B depicts a schematic top view of an exemplary physiologicalmeasurement system 100 including a head portion 104 and a strap 102. Thehead portion 104 and/or the strap 102 may include a circuit boardincluding a plurality of integrated circuit boards or chips 820, 822,824 forming a multi-chip module assembled in a horizontally spacedconfiguration. The integrated circuit chips are electrically coupled toone another using wiring 826. The circuit chips may be distributedthrough the head portion and/or the strap of the system. In thenon-limiting illustrative embodiment, for example, one chip is providedin the head portion and two chips are provided in the strap.

Exemplary systems include a processing module configured to filter theraw photoplethysmography data received from the light detectors tominimize contributions due to motion, and subsequently process thefiltered data to detect peaks in the data that correspond with heartbeats of a user. The overall algorithm for detecting heart beats takesas input the analog signals from optical sensors (mV) and accelerometer,and outputs an implied beats per minute (heart rate) of the signalaccurate within a few beats per minute as that determined by anelectrocardiography machine even during motion.

In one aspect, using multiple LEDs with different wavelengths reactingto movement in different ways can allow for signal recovery withstandard signal processing techniques. The availability of accelerometerinformation can also be used to compensate for coarse movement signalcorruption. In order to increase the range of movements that thealgorithm can successfully filter out, an aspect utilizes techniquesthat augment the algorithm already in place. For example, filteringviolent movements of the arm during very short periods of time, such asboxing as exercising, may be utilized by the system. By selectivesampling and interpolating over these impulses, an aspect can accountfor more extreme cases of motion. Additionally, an investigation intodifferent LED wavelengths, intensities, and configurations can allow thesystems described herein to extract a signal across a wide spectrum ofskin types and wrist sizes. In other words, motion filtering algorithmsand signal processing techniques may assist in mitigating the riskcaused by movement.

FIG. 9 is a flowchart illustrating an exemplary signal processingalgorithm for generating a sequence of heart rates for every detectedheartbeat that is embodied in computer-executable instructions stored onone or more non-transitory computer-readable media. In step 902, lightemitters of a wearable physiological measurement system emit lighttoward a user's skin. In step 904, light reflected from the user's skinis detected at the light detectors in the system. In step 906, signalsor data associated with the reflected light are pre-processed using anysuitable technique to facilitate detection of heart beats. In step 908,a processing module of the system executes one or morecomputer-executable instructions associated with a peak detectionalgorithm to process data corresponding to the reflected light to detecta plurality of peaks associated with a plurality of beats of the user'sheart. In step 910, the processing module determines an RR intervalbased on the plurality of peaks detected by the peak detectionalgorithm. In step 912, the processing module determines a confidencelevel associated with the RR interval.

Based on the confidence level associated with the RR interval estimate,the processing module selects either the peak detection algorithm or afrequency analysis algorithm to process data corresponding to thereflected light to determine the sequence of instantaneous heart ratesof the user. The frequency analysis algorithm may process the datacorresponding to the reflected light based on the motion of the userdetected using, for example, an accelerometer. The processing module mayselect the peak detection algorithm or the frequency analysis algorithmregardless of a motion status of the user. It is advantageous to use theconfidence in the estimate in deciding whether to switch tofrequency-based methods as certain frequency-based approaches are unableto obtain accurate RR intervals for heart rate variability analysis.Therefore, an implementation maintains the ability to obtain the RRintervals for as long as possible, even in the case of motion, therebymaximizing the information that can be extracted.

For example, in step 914, it is determined whether the confidence levelassociated with the RR interval is above (or equal to or above) athreshold. In certain embodiments, the threshold may be predefined, forexample, about 50%-90% in some embodiments and about 80% in onenon-limiting embodiment. In other embodiments, the threshold may beadaptive, i.e., the threshold may be dynamically and automaticallydetermined based on previous confidence levels. For example, if one ormore previous confidence levels were high (i.e., above a certain level),the system may determine that a present confidence level that isrelatively low compared to the previous levels is indicative of a lessreliable signal. In this case, the threshold may be dynamically adjustedto be higher so that a frequency-based analysis method may be selectedto process the less reliable signal.

If the confidence level is above (or equal to or above) the threshold,in step 916, the processing module may use the plurality of peaks todetermine an instantaneous heart rate of the user. On the other hand, instep 920, based on a determination that the confidence level associatedwith the RR interval is equal to or below the predetermined threshold,the processing module may execute one or more computer-executableinstructions associated with the frequency analysis algorithm todetermine an instantaneous heart rate of the user. The confidencethreshold may be dynamically set based on previous confidence levels.

In some embodiments, in steps 918 or 922, the processing moduledetermines a heart rate variability of the user based on the sequence ofthe instantaneous heart rates/beats.

The system may include a display device configured to render a userinterface for displaying the sequence of the instantaneous heart ratesof the user, the RR intervals and/or the heart rate variabilitydetermined by the processing module. The system may include a storagedevice configured to store the sequence of the instantaneous heartrates, the RR intervals and/or the heart rate variability determined bythe processing module.

In one aspect, the system may switch between different analyticaltechniques for determining a heart rate such as a statistical techniquefor detecting a heart rate and a frequency domain technique fordetecting a heart rate. These two different modes have differentadvantages in terms of accuracy, processing efficiency, and informationcontent, and as such may be useful at different times and underdifferent conditions. Rather than selecting one such mode or techniqueas an attempted optimization, the system may usefully switch back andforth between these differing techniques, or other analyticaltechniques, using a predetermined criterion. For example, wherestatistical techniques are used, a confidence level may be determinedand used as a threshold for switching to an alternative technique suchas a frequency domain technique. The threshold may also or insteaddepend on historical, subjective, and/or adapted data for a particularuser. For example, selection of a threshold may depend on data for aparticular user including without limitation subjective informationabout how a heart rate for a particular user responds to stress,exercise, and so forth. Similarly, the threshold may adapt to changes infitness of a user, context provided from other sensors of the wearablesystem, signal noise, and so forth.

An exemplary statistical technique employs probabilistic peak detection.In this technique, a discrete probabilistic step may be set, and alikelihood function may be established as a mixture of a Gaussian randomvariable and a uniform. The heart of the likelihood function encodes theassumption that with a first probability (p) the peak detectionalgorithm has produced a reasonable initial estimate, but with a secondprobability (1−p) it has not. In a subsequent step, Bayes' rule isapplied to determine the posterior density on the parameter space, ofwhich the maximum is taken (that is, the argument (parameter) thatmaximizes the posterior distribution). This value is the estimate forthe heart rate. In a subsequent step, the previous two steps arereapplied for the rest of the sample. There is some variance in thesignal due to process noise, which is dependent on the length of theinterval. This process noise becomes the variance in the Gaussians usedfor the likelihood function. Then, the estimate is obtained as themaximum a posteriori on the new posterior distribution. A confidencevalue is recorded for the estimate which, for some precisionmeasurement, the posterior value is summed at points in the parameterspace centered at our estimate+/−the precision.

The beats per minute (BPM) parameter space, θ, may range between about20 and about 240, corresponding to the empirical bounds on human heartrates. In an exemplary method, a probability distribution is calculatedover this parameter space, at each step declaring the mode of thedistribution to be the heart rate estimate. A discrete uniform prior maybe set:

π₁˜DiscUnif(θ)

The un-normalized, univariate likelihood is defined by a mixture of aGaussian function and a uniform:

l₁˜IG+(1−I)U, G˜N(γ₁σ²), I˜Ber(p)

where

U˜DiscUnif(θ)

and where σ and p are predetermined constants.

Bayes' rule is applied to determine the posterior density on θ, forexample, by component-wise multiplying the prior density vector(π₁(θ))_(θϵθ) with the likelihood vector (l₁(θ))_(θϵθ) to obtain theposterior distribution η₁. Then, the following is set:

β₁=argmax_(θϵθ)η₁(θ)

For k≥2, the variance in signal S(t) due to process noise is determined.Then, the following variable is set to imbue temporally long RRintervals with more process/interpeak noise and set thepost-normalization convolution:

π_(k)=η_(k−1) *f _(N(o, λ) _(k) ₂ _()θ)

where f is a density function of the following:

Z ∼ N(o, λ_(k)²)

Then, the following expressions are calculated:

l_(k)˜pG_(k)+(1−p)U, G_(k)˜N(λk, σ²)

The expression is then normalized and recorded:

β_(k)=argmax_(θϵθ)η_(k)(θ)

Finally, the confidence level of the above expression for a particularprecision threshold is determined:

$C_{k} = {\sum\limits_{\theta \in {{\lbrack{{\beta_{k} - e_{1}},{\beta_{k} + e}}\rbrack}\theta}}{\eta_{k}.}}$

An exemplary frequency analysis algorithm used in an implementationisolates the highest frequency components of the optical data, checksfor harmonics common in both the accelerometer data and the opticaldata, and performs filtering of the optical data. The algorithm takes asinput raw analog signals from the accelerometer (3-axis) and pulsesensors, and outputs heart rate values or beats per minute (BPM) for agiven period of time related to the window of the spectrogram.

The isolation of the highest frequency components is performed in aplurality of stages, gradually winnowing the window-sizes ofconsideration, thereby narrowing the range of errors. In oneimplementation, a spectrogram of 2{circumflex over ( )}15 samples withoverlap 2{circumflex over ( )}13 samples of the optical data isgenerated. The spectrogram is restricted to frequencies in which heartrate can lie. These restriction boundaries may be updated when smallerwindow sizes are considered. The frequency estimate is extracted fromthe spectrogram by identifying the most prominent frequency component ofthe spectrogram for the optical data. The frequency may be extractedusing the following exemplary steps. The most prominent frequency of thespectrogram is identified in the signal. It is determined if thefrequency estimate is a harmonic of the true frequency. The frequencyestimate is replaced with the true frequency if the estimate is aharmonic of the true frequency. It is determined if the currentfrequency estimate is a harmonic of the motion sensor data. Thefrequency estimate is replaced with a previous temporal estimate if itis a harmonic of the motion sensor data. The upper and lower bounds onthe frequency obtained are saved. A constant value may be added orsubtracted in some cases. In subsequent steps, the constant added orsubtracted may be reduced to provide narrower searches. A number of theprevious steps are repeated one or more times, e.g., three times, excepttaking 2{circumflex over ( )}{15-i} samples for the window size and2{circumflex over ( )}{13-i} for the overlap in the spectrogram where iis the current number of iteration. The final output is the average ofthe final symmetric endpoints of the frequency estimation.

The table below demonstrates the performance of the algorithm disclosedherein. To arrive at the results below, experiments were conducted inwhich a subject wore an exemplary wearable physiological measurementsystem and a 3-lead ECG which were both wired to the samemicrocontroller (e.g., Arduino) in order to provide time-synced data.Approximately 50 data sets were analyzed which included the subjectstanding still, walking, and running on a treadmill.

TABLE 1 Performance of signal processing algorithm disclosed hereinClean data error Noisy data error (mean, std) in BPM (mean, std) in BPM4-level spectrogram 0.2, 2.3 0.8, 5.1 (80 second blocks)

The algorithm's performance comes from a combination of a probabilisticand frequency based approach. The three difficulties in creatingalgorithms for heart rate calculations from the PPG data are 1) falsedetections of beats, 2) missed detections of real beats, and 3) errorsin the precise timing of the beat detection. The algorithms disclosedherein provide improvements in these three sources of error and, in somecases, the error is bound to within 2 BPM of ECG values at all timeseven during the most motion intense activities.

The exemplary wearable system computes heart rate variability (HRV) toobtain an understanding of the recovery status of the body. These valuesare captured right before a user awakes or when the user is not moving,in both cases photoplethysmography (PPG) variability yieldingequivalence to the ECG HRV. HRV is traditionally measured using an ECGmachine and obtaining a time series of R-R intervals. Because anexemplary wearable system utilizes photoplethysmography (PPG), it doesnot obtain the electric signature from the heart beats; instead, thepeaks in the obtained signal correspond to arterial blood volume. Atrest, these peaks are directly correlated with cardiac cycles, whichenables the calculation of HRV via analyzing peak-to-peak intervals (thePPG analog of RR intervals). It has been demonstrated in the medicalliterature that these peak-to-peak intervals, the “PPG variability,” isidentical to ECG HRV while at rest. See, Charlot K, et al.“Interchangeability between heart rate and photoplethysmographyvariabilities during sympathetic stimulations.” PhysiologicalMeasurement. 2009 December; 30(12): 1357-69. doi:10.1088/0967-3334/30/12/005. URL:http://www.ncbi.nlm.nih.gov/pubmed/19864707; and Lu, S, et. al. “Canphotoplethysmography variability serve as an alternative approach toobtain heart rate variability information?” Journal of ClinicalMonitoring and Computing. 2008 February; 22(1):23-9. URL:http://www.ncbi.nlm.nih.gov/pubmed/17987395, the entire contents ofwhich are incorporated herein by reference.

Exemplary physiological measurement systems are configured to minimizepower consumption so that the systems may be worn continuously withoutrequiring power recharging at frequent intervals. The majority ofcurrent draw in an exemplary system is allocated to power the lightemitters, e.g., LEDs, the wireless transceiver, the microcontroller andperipherals. In one embodiment, the circuit board of the system mayinclude a boost converter that runs a current of about 10 mA througheach of the light emitters with an efficiency of about 80% and may drawpower directly from the batteries at substantially constant power. Withexemplary batteries at about 3.7 V, the current draw from the batterymay be about 40 mW. In some embodiments, the wireless transceiver maydraw about 10-20 mA of current when it is actively transferring data. Insome embodiments, the microcontroller and peripherals may draw about 5mA of current.

An exemplary system may include a processing module that is configuredto automatically adjust one or more operational characteristics of thelight emitters and/or the light detectors to minimize power consumptionwhile ensuring that all heart beats of the user are reliably andcontinuously detected. The operational characteristics may include, butare not limited to, a frequency of light emitted by the light emitters,the number of light emitters activated, a duty cycle of the lightemitters, a brightness of the light emitters, a sampling rate of thelight detectors, and the like.

The processing module may adjust the operational characteristics basedon one or more signals or indicators obtained or derived from one ormore sensors in the system including, but not limited to, a motionstatus of the user, a sleep status of the user, historical informationon the user's physiological and/or habits, an environmental orcontextual condition (e.g., ambient light conditions), a physicalcharacteristic of the user (e.g., the optical characteristics of theuser's skin), and the like.

In one embodiment, the processing module may receive data on the motionof the user using, for example, an accelerometer. The processing modulemay process the motion data to determine a motion status of the userwhich indicates the level of motion of the user, for example, exercise,light motion (e.g., walking), no motion or rest, sleep, and the like.The processing module may adjust the duty cycle of one or more lightemitters and the corresponding sampling rate of the one or more lightdetectors based on the motion status. For example, upon determining thatthe motion status indicates that the user is at a first higher level ofmotion, the processing module may activate the light emitters at a firsthigher duty cycle and sample the reflected light using light detectorssampling at a first higher sampling rate. Upon determining that themotion status indicates that the user is at a second lower level ofmotion, the processing module may activate the light emitters at asecond lower duty cycle and sample the reflected light using lightdetectors sampling at a second lower sampling rate. That is, the dutycycle of the light emitters and the corresponding sampling rate of thelight detectors may be adjusted in a graduated or continuous mannerbased on the motion status or level of motion of the user. Thisadjustment ensures that heart rate data is detected at a sufficientlyhigh frequency during motion to reliably detect all of the heart beatsof the user.

In non-limiting examples, the light emitters may be activated at a dutycycle ranging from about 1% to about 100%. In another example, the lightemitters may be activated at a duty cycle ranging from about 20% toabout 50% to minimize power consumption. Certain exemplary samplingrates of the light detectors may range from about 50 Hz to about 1000Hz, but are not limited to these exemplary rates. Certain non-limitingsampling rates are, for example, about 100 Hz, 200 Hz, 500 Hz, and thelike.

In one non-limiting example, the light detectors may sample continuouslywhen the user is performing an exercise routine so that the errorstandard deviation is kept within 5 beats per minute (BPM). When theuser is at rest, the light detectors may be activated for about a 1%duty cycle—10 milliseconds each second (i.e., 1% of the time) so thatthe error standard deviation is kept within 5 BPM (including an errorstandard deviation in the heart rate measurement of 2 BPM and an errorstandard deviation in the heart rate changes between measurement of 3BPM). When the user is in light motion (e.g., walking), the lightdetectors may be activated for about a 10% duty cycle—100 millisecondseach second (i.e., 10% of the time) so that the error standard deviationis kept within 6 BPM (including an error standard deviation in the heartrate measurement of 2 BPM and an error standard deviation in the heartrate changes between measurement of 4 BPM).

The processing module may adjust the brightness of one or more lightemitters by adjusting the current supplied to the light emitters. Forexample, a first level of brightness may be set by current rangingbetween about 1 mA to about 10 mA, but is not limited to this exemplaryrange. A second higher level of brightness may be set by current rangingfrom about 11 mA to about 30 mA, but is not limited to this exemplaryrange. A third higher level of brightness may be set by current rangingfrom about 80 mA to about 120 mA, but is not limited to this exemplaryrange. In one non-limiting example, first, second and third levels ofbrightness may be set by current of about 5 mA, about 20 mA and about100 mA, respectively.

In some embodiments, the processing module may detect an environmentalor contextual condition (e.g., level of ambient light) and adjust thebrightness of the light emitters accordingly to ensure that the lightdetectors reliably detect light reflected from the user's skin whileminimizing power consumption. For example, if it is determined that theambient light is at a first higher level, the brightness of the lightemitters may be set at a first higher level. If it is determined thatthe ambient light is at a second lower level, the brightness of thelight emitters may be set at a second lower level. In some cases, thebrightness may be adjusted in a continuous manner based on the detectedenvironment condition.

In some embodiments, the processing module may detect a physiologicalcondition of the user (e.g., an optical characteristic of the user'sskin) and adjust the brightness of the light emitters accordingly toensure that the light detectors reliably detect light reflected from theuser's skin while minimizing power consumption. For example, if it isdetermined that the user's skin is highly reflective, the brightness ofthe light emitters may be set at a first lower level. If it isdetermined that the user's skin is not very reflective, the brightnessof the light emitters may be set at a second higher level.

Shorter-wavelength LEDs may require more power than is required bylonger-wavelength LEDs. Therefore, an exemplary wearable system mayprovide and use light emitted at two or more different frequencies basedon the level of motion detected in order to save battery life. Forexample, upon determining that the motion status indicates that the useris at a first higher level of motion (e.g., exercising), one or morelight emitters may be activated to emit light at a first wavelength.Upon determining that the motion status indicates that the user is at asecond lower level of motion (e.g., at rest), one or more light emittersmay be activated to emit light at a second wavelength that is longerthan the first wavelength. Upon determining that the motion statusindicates that the user is at a third lower level of motion (e.g.,sleeping), one or more light emitters may be activated to emit light ata third wavelength that is longer than the first and second wavelengths.Other levels of motion may be predetermined and correspondingwavelengths of emitted light may be selected. The threshold levels ofmotion that trigger adjustment of the light wavelength may be based onone or more factors including, but are not limited to, skin properties,ambient light conditions, and the like. Any suitable combination oflight wavelengths may be selected, for example, green (for a higherlevel of motion)/red (for a lower level of motion); red (for a higherlevel of motion)/infrared (for a lower level of motion); blue (for ahigher level of motion)/green (for a lower level of motion); and thelike.

Shorter-wavelength LEDs may require more power than is required by othertypes of heart rate sensors, such as, a piezo-sensor or an infraredsensor. Therefore, an exemplary wearable system may provide and use aunique combination of sensors—one or more light detectors for periodswhere motion is expected and one or more piezo and/or infrared sensorsfor low motion periods (e.g., sleep)—to save battery life. Certain otherembodiments of a wearable system may exclude piezo-sensors and/orinfrared sensors.

For example, upon determining that the motion status indicates that theuser is at a first higher level of motion (e.g., exercising), one ormore light emitters may be activated to emit light at a firstwavelength. Upon determining that the motion status indicates that theuser is at a second lower level of motion (e.g., at rest), non-lightbased sensors may be activated. The threshold levels of motion thattrigger adjustment of the type of sensor may be based on one or morefactors including, but are not limited to, skin properties, ambientlight conditions, and the like.

The system may determine the type of sensor to use at a given time basedon the level of motion (e.g., via an accelerometer) and whether the useris asleep (e.g., based on movement input, skin temperature and heartrate). Based on a combination of these factors the system selectivelychooses which type of sensor to use in monitoring the heart rate of theuser. Common symptoms of being asleep are periods of no movement orsmall bursts of movement (such as shifting in bed), lower skintemperature (although it is not a dramatic drop from normal), drasticGSR changes, and heart rate that is below the typical resting heart ratewhen the user is awake. These variables depend on the physiology of aperson and thus a machine learning algorithm is trained withuser-specific input to determine when he/she is awake/asleep anddetermine from that the exact parameters that cause the algorithm todeem someone asleep.

In an exemplary configuration, the light detectors may be positioned onthe underside of the wearable system and all of the heart rate sensorsmay be positioned adjacent to each other. For example, the low powersensor(s) may be adjacent to the high power sensor(s) as the sensors maybe chosen and placed where the strongest signal occurs. In one exampleconfiguration, a 3-axis accelerometer may be used that is located on thetop part of the wearable system.

In some embodiments, the processing module may be configured toautomatically adjust a rate at which data is transmitted by the wirelesstransmitter to minimize power consumption while ensuring that raw andprocessed data generated by the system is reliably transmitted toexternal computing devices. In one embodiment, the processing moduledetermines an amount of data to be transmitted (e.g., based on theamount of data generated since the time of the last data transmission),and may select the next data transmission time based on the amount ofdata to be transmitted. For example, if it is determined that the amountof data exceeds (or is equal to or greater than) a threshold level, theprocessing module may transmit the data or may schedule a time fortransmitting the data. On the other hand, if it is determined that theamount of data does not exceed (or is equal to or lower than) thethreshold level, the processing module may postpone data transmission tominimize power consumption by the transmitter. In one non-limitingexample, the threshold may be set to the amount of data that may be sentin two seconds under current conditions. Exemplary data transmissionrates may range from about 50 kbytes per second to about 1 MByte persecond, but are not limiting to this exemplary range.

In some embodiments, an operational characteristic of the microprocessormay be automatically adjusted to minimize power consumption. Thisadjustment may be based on a level of motion of the user's body.

More generally, the above description contemplates a variety oftechniques for sensing conditions relating to heart rate monitoring orrelated physiological activity either directly (e.g., confidence levelsor accuracy of calculated heart rate) or indirectly (e.g., motiondetection, temperature). However measured, these sensed conditions canbe used to intelligently select from among a number of different modes,including hardware modes, software modes, and combinations of theforegoing, for monitoring heart rate based on, e.g., accuracy, powerusage, detected activity states, and so forth. Thus there is disclosedherein techniques for selecting from among two or more different heartrate monitoring modes according to a sensed condition.

III. EXEMPLARY PHYSIOLOGICAL ANALYTICS SYSTEM

Exemplary embodiments provide an analytics system for providingqualitative and quantitative monitoring of a user's body, health andphysical training. The analytics system is implemented incomputer-executable instructions encoded on one or more non-transitorycomputer-readable media. The analytics system relies on and usescontinuous data on one or more physiological parameters including, butnot limited to, heart rate. The continuous data used by the analyticssystem may be obtained or derived from an exemplary physiologicalmeasurement system disclosed herein, or may be obtained or derived froma derived source or system, for example, a database of physiologicaldata. In some embodiments, the analytics system computes, stores anddisplays one or more indicators or scores relating to the user's body,health and physical training including, but not limited to, an intensityscore and a recovery score. The scores may be updated in real-time andcontinuously or at specific time periods, for example, the recoveryscore may be determined every morning upon waking up, the intensityscore may be determined in real-time or after a workout routine or foran entire day.

In certain exemplary embodiments, a fitness score may be automaticallydetermined based on the physiological data of two or more users ofexemplary wearable systems.

An intensity score or indicator provides an accurate indication of thecardiovascular intensities experienced by the user during a portion of aday, during the entire day or during any desired period of time (e.g.,during a week or month). The intensity score is customized and adaptedfor the unique physiological properties of the user and takes intoaccount, for example, the user's age, gender, anaerobic threshold,resting heart rate, maximum heart rate, and the like. If determined foran exercise routine, the intensity score provides an indication of thecardiovascular intensities experienced by the user continuouslythroughout the routine. If determined for a period of including andbeyond an exercise routine, the intensity score provides an indicationof the cardiovascular intensities experienced by the user during theroutine and also the activities the user performed after the routine(e.g., resting on the couch, active day of shopping) that may affecttheir recovery or exercise readiness.

In exemplary embodiments, the intensity score is calculated based on theuser's heart rate reserve (HRR) as detected continuously throughout thedesired time period, for example, throughout the entire day. In oneembodiment, the intensity score is an integral sum of the weighted HRRdetected continuously throughout the desired time period. FIG. 10 is aflowchart illustrating an exemplary method of determining an intensityscore.

In step 1002, continuous heart rate readings are converted to HRRvalues. A time series of heart rate data used in step 1002 may bedenoted as:

H ∈ T

-   -   A time series of HRR measurements, v(t), may be defined in the        following expression in which MHR is the maximum heart rate and        RHR is the resting heart rate of the user:

${v(t)} = \frac{{H(t)} - {RHR}}{{MHR} - {RHR}}$

In step 1004, the HRR values are weighted according to a suitableweighting scheme. Cardiovascular intensity, indicated by an intensityscore, is defined in the following expression in which w is a weightingfunction of the HRR measurements:

I(t _(o) , t ₁)=∫_(t) ₀ ^(t) ¹ w(v(t))dt

In step 1006, the weighted time series of HRR values is summed andnormalized.

I _(t)=∫_(T) w(v(t))dt≤w(1)|T|

Thus, the weighted sum is normalized to the unit interval, i.e., [0, 1]:

$N_{T} = \frac{I_{T}}{{{w(1)} \cdot 24}{hr}}$

In step 1008, the summed and normalized values are scaled to generateuser-friendly intensity score values. That is, the unit interval istransformed to have any desired distribution in a scale (e.g., a scaleincluding 21 points from 0 to 21), for example, arctangent, sigmoid,sinusoidal, and the like. In certain distributions, the intensity valuesincrease at a linear rate along the scale, and in others, at the highestranges the intensity values increase at more than a linear rate toindicate that it is more difficult to climb in the scale toward theextreme end of the scale. In some embodiments, the raw intensity scoresare scaled by fitting a curve to a selected group of “canonical”exercise routines that are predefined to have particular intensityscores.

In one embodiment, monotonic transformations of the unit interval areachieved to transform the raw HRR values to user-friendly intensityscores. An exemplary scaling scheme, expressed as f: [0, 1]→[0, 1], isperformed using the following function:

$\left( {x,N,p} \right) = {0.5\left( {\frac{\arctan\left( {N\left( {x - p} \right)} \right)}{\pi/2} + 1} \right)}$

To generate an intensity score, the resulting value may be multiplied bya number based on the desired scale of the intensity score. For example,if the intensity score is graduated from zero to 21, then the value maybe multiplied by 21.

In step 1010, the intensity score values are stored on a non-transitorystorage medium for retrieval, display and usage. In step 1012, theintensity score values are, in some embodiments, displayed on a userinterface rendered on a visual display device. The intensity scorevalues may be displayed as numbers and/or with the aid of graphicaltools, e.g., a graphical display of the scale of intensity scores withcurrent score, and the like. In some embodiments, the intensity scoremay be indicated by audio. In step 1012, the intensity score values are,in some embodiments, displayed along with one or more quantitative orqualitative pieces of information on the user including, but not limitedto, whether the user has exceeded his/her anaerobic threshold, the heartrate zones experienced by the user during an exercise routine, howdifficult an exercise routine was in the context of the user's training,the user's perceived exertion during an exercise routine, whether theexercise regimen of the user should be automatically adjusted (e.g.,made easier if the intensity scores are consistently high), whether theuser is likely to experience soreness the next day and the level ofexpected soreness, characteristics of the exercise routine (e.g., howdifficult it was for the user, whether the exercise was in bursts oractivity, whether the exercise was tapering, etc.), and the like. In oneembodiment, the analytics system may automatically generate, store anddisplay an exercise regimen customized based on the intensity scores ofthe user.

Step 1006 may use any of a number of exemplary static or dynamicweighting schemes that enable the intensity score to be customized andadapted for the unique physiological properties of the user. In oneexemplary static weighting scheme, the weights applied to the HRR valuesare based on static models of a physiological process. The human bodyemploys different sources of energy with varying efficiencies andadvantages at different HRR levels. For example, at the anaerobicthreshold (AT), the body shifts to anaerobic respiration in which thecells produce two adenosine triphosphate (ATP) molecules per glucosemolecule, as opposed to 36 at lower HRR levels. At even higher HRRlevels, there is a further subsequent threshold (CPT) at which creatinetriphosphate (CTP) is employed for respiration with even lessefficiency.

In order to account for the differing levels of cardiovascular exertionand efficiency at the different HRR levels, in one embodiment, thepossible values of HRR are divided into a plurality of categories,sections or levels (e.g., three) dependent on the efficiency of cellularrespiration at the respective categories. The HRR parameter range may bedivided in any suitable manner, such as, piecewise, includingpiecewise-linear, piecewise-exponential, and the like. An exemplarypiecewise-linear division of the HRR parameter range enables weightingeach category with strictly increasing values. This scheme captures anaccurate indication of the cardiovascular intensity experienced by theuser because it is more difficult to spend time at higher HRR values,which suggests that the weighting function should increase at theincreasing weight categories.

In one non-limiting example, the HRR parameter range may be considered arange from zero (0) to one (1) and divided into categories with strictlyincreasing weights. In one example, the HRR parameter range may bedivided into a first category of a zero HRR value and may assign thiscategory a weight of zero; a second category of HRR values fallingbetween zero (0) and the user's anaerobic threshold (AT) and may assignthis category a weight of one (1); a third category of HRR valuesfalling between the user's anaerobic threshold (AT) and a threshold atwhich the user's body employs creatine triphosphate for respiration(CPT) and may assign this category a weight of 18; and a fourth categoryof HRR values falling between the creatine triphosphate threshold (CPT)and one (1) and may assign this category a weight of 42, although othernumbers of HRR categories and different weight values are possible. Thatis, in this example, the weights are defined as:

0 : ν = 0 1 : ν ∈ (0, AT] w(ν) = {open oversize brace} 18 : ν ∈ (AT,CPT] 42 : ν ∈ (CPT, 1]

In another exemplary embodiment of the weighting scheme, the HRR timeseries is weighted iteratively based on the intensity scores determinedthus far (e.g., the intensity score accrued thus far) and the path takenby the HRR values to get to the present intensity score. The path may bedetected automatically based on the historical HRR values and mayindicate, for example, whether the user is performing high intensityinterval training (during which the intensity scores are rapidly risingand falling), whether the user is taking long breaks between bursts ofexercise (during which the intensity scores are rising after longerperiods), and the like. The path may be used to dynamically determineand adjust the weights applied to the HRR values. For example, in thecase of high intensity interval training, the weights applied may behigher than in the case of a more traditional exercise routine.

In another exemplary embodiment of the weighting scheme, a predictiveapproach is used by modeling the weights or coefficients to be thecoefficient estimates of a logistic regression model. In this scheme, atraining data set is obtained by continuously detecting the heart ratetime series and other personal parameters of a group of individuals. Thetraining data set is used to train a machine learning system to predictthe cardiovascular intensities experienced by the individuals based onthe heart rate and other personal data. The trained system models aregression in which the coefficient estimates correspond to the weightsor coefficients of the weighting scheme. In the training phase, userinput on perceived exertion and the intensity scores are compared. Thelearning algorithm also alters the weighs based on the improving ordeclining health of a user as well as their qualitative feedback. Thisyields a unique algorithm that incorporates physiology, qualitativefeedback, and quantitative data. In determining a weighting scheme for aspecific user, the trained machine learning system is run by executingcomputer-executable instructions encoded on one or more non-transitorycomputer-readable media, and generates the coefficient estimates whichare then used to weight the user's HRR time series.

One of ordinary skill in the art will recognize that two or more aspectsof any of the disclosed weighting schemes may be applied separately orin combination in an exemplary method for determining an intensityscore.

In one aspect, heart rate zones quantify the intensity of workouts byweighing and comparing different levels of heart activity as percentagesof maximum heart rate. Analysis of the amount of time an individualspends training at a certain percentage of his/her MHR may revealhis/her state of physical exertion during a workout. This intensity,developed from the heart rate zone analysis, motion, and activity, maythen indicate his/her need for rest and recovery after the workout,e.g., to minimize delayed onset muscle soreness (DOMS) and preparehim/her for further activity. As discussed above, MHR, heart rate zones,time spent above the anaerobic threshold, and HRV in RSA (RespiratorySinus Arrhythmia) regions—as well as personal information (gender, age,height, weight, etc.) may be utilized in data processing.

A recovery score or indicator provides an accurate indication of thelevel of recovery of a user's body and health after a period of physicalexertion. The human autonomic nervous system controls the involuntaryaspects of the body's physiology and is typically subdivided into twobranches: parasympathetic (deactivating) and sympathetic (activating).Heart rate variability (HRV), i.e., the fluctuation in inter-heartbeatinterval time, is a commonly studied result of the interplay betweenthese two competing branches. Parasympathetic activation reflects inputsfrom internal organs, causing a decrease in heart rate. Sympatheticactivation increases in response to stress, exercise and disease,causing an increase in heart rate. For example, when high intensityexercise takes place, the sympathetic response to the exercise persistslong after the completion of the exercise. When high intensity exerciseis followed by insufficient recovery, this imbalance lasts typicallyuntil the next morning, resulting in a low morning HRV. This resultshould be taken as a warning sign as it indicates that theparasympathetic system was suppressed throughout the night. Whilesuppressed, normal repair and maintenance processes that ordinarilywould occur during sleep were suppressed as well. Suppression of thenormal repair and maintenance processes results in an unprepared statefor the next day, making subsequent exercise attempts more challenging.

The recovery score is customized and adapted for the uniquephysiological properties of the user and takes into account, forexample, the user's heart rate variability (HRV), resting heart rate,sleep quality and recent physiological strain (indicated, in oneexample, by the intensity score of the user). In one exemplaryembodiment, the recovery score is a weighted combination of the user'sheart rate variability (HRV), resting heart rate, sleep qualityindicated by a sleep score, and recent strain (indicated, in oneexample, by the intensity score of the user). In an exemplar, the sleepscore combined with performance readiness measures (such as, morningheart rate and morning heart rate variability) provides a completeoverview of recovery to the user. By considering sleep and HRV alone orin combination, the user can understand how exercise-ready he/she iseach day and to understand how he/she arrived at the exercise-readinessscore each day, for example, whether a low exercise-readiness score is apredictor of poor recovery habits or an inappropriate training schedule.This insight aids the user in adjusting his/her daily activities,exercise regimen and sleeping schedule therefore obtain the most out ofhis/her training.

In some cases, the recovery score may take into account perceivedpsychological strain experienced by the user. In some cases, perceivedpsychological strain may be detected from user input via, for example, aquestionnaire on a mobile device or web application. In other cases,psychological strain may be determined automatically by detectingchanges in sympathetic activation based on one or more parametersincluding, but not limited to, heart rate variability, heart rate,galvanic skin response, and the like.

With regard to the user's HRV used in determining the recovery score,suitable techniques for analyzing HRV include, but are not limited to,time-domain methods, frequency-domain methods, geometric methods andnon-linear methods. In one embodiment, the HRV metric of theroot-mean-square of successive differences (RMSSD) of RR intervals isused. The analytics system may consider the magnitude of the differencesbetween 7-day moving averages and 3-day moving averages of thesereadings for a given day. Other embodiments may use Poincaré Plotanalysis or other suitable metrics of HRV.

The recovery score algorithm may take into account RHR along withhistory of past intensity and recovery scores.

With regard to the user's resting heart rate, moving averages of theresting heart rate are analyzed to determine significant deviations.Consideration of the moving averages is important since day-to-dayphysiological variation is quite large even in healthy individuals.Therefore, the analytics system may perform a smoothing operation todistinguish changes from normal fluctuations.

Although an inactive condition, sleep is a highly active recovery stateduring which a major portion of the physiological recovery process takesplace. Nonetheless, a small, yet significant, amount of recovery canoccur throughout the day by rehydration, macronutrient replacement,lactic acid removal, glycogen re-synthesis, growth hormone productionand a limited amount of musculoskeletal repair. In assessing the user'ssleep quality, the analytics system generates a sleep score usingcontinuous data collected by an exemplary physiological measurementsystem regarding the user's heart rate, skin conductivity, ambienttemperature and accelerometer/gyroscope data throughout the user'ssleep. Collection and use of these four streams of data enable anunderstanding of sleep previously only accessible through invasive anddisruptive over-night laboratory testing. For example, an increase inskin conductivity when ambient temperature is not increasing, thewearer's heart rate is low, and the accelerometer/gyroscope shows littlemotion, may indicate that the wearer has fallen asleep. The sleep scoreindicates and is a measure of sleep efficiency (how good the user'ssleep was) and sleep duration (if the user had sufficient sleep). Eachof these measures is determined by a combination of physiologicalparameters, personal habits and daily stress/strain (intensity) inputs.The actual data measuring the time spent in various stages of sleep maybe combined with the wearer's recent daily history and a longer-termdata set describing the wearer's personal habits to assess the level ofsleep sufficiency achieved by the user. The sleep score is designed tomodel sleep quality in the context of sleep duration and history. Itthus takes advantage of the continuous monitoring nature of theexemplary physiological measurement systems disclosed herein byconsidering each sleep period in the context of biologically-determinedsleep needs, pattern-determined sleep needs and historically-determinedsleep debt.

The recovery and sleep score values are stored on a non-transitorystorage medium for retrieval, display and usage. The recovery and/orsleep score values are, in some embodiments, displayed on a userinterface rendered on a visual display device. The recovery and/or sleepscore values may be displayed as numbers and/or with the aid ofgraphical tools, e.g., a graphical display of the scale of recoveryscores with current score, and the like. In some embodiments, therecovery and/or sleep score may be indicated by audio. The recoveryscore values are, in some embodiments, displayed along with one or morequantitative or qualitative pieces of information on the user including,but not limited to, whether the user has recovered sufficiently, whatlevel of activity the user is prepared to perform, whether the user isprepared to perform an exercise routine a particular desired intensity,whether the user should rest and the duration of recommended rest,whether the exercise regimen of the user should be automaticallyadjusted (e.g., made easier if the recovery score is low), and the like.In one embodiment, the analytics system may automatically generate,store and display an exercise regimen customized based on the recoveryscores of the user alone or in combination with the intensity scores.

As discussed above, the sleep performance metric may be based onparameters like the number of hours of sleep, sleep onset latency, andthe number of sleep disturbances. In this manner, the score may comparea tactical athlete's duration and quality of sleep in relation to thetactical athlete's evolving sleep need (e.g., a number of hours based onrecent strain, habitual sleep need, signs of sickness, and sleep debt).By way of example, a soldier may have a dynamically changing need forsleep, and it may be important to consider the total hours of sleep inrelation to the amount of sleep that may have been required. Byproviding an accurate sensor for sleep and sleep performance, an aspectmay evaluate sleep in the context of the overall day and lifestyle of aspecific user.

FIG. 11 is a flowchart illustrating an exemplary method by which a usermay use intensity and recovery scores. In step 1102, the wearablephysiological measurement system begins determining heart ratevariability (HRV) measurements based on continuous heart rate datacollected by an exemplary physiological measurement system. In somecases, it may take the collection of several days of heart rate data toobtain an accurate baseline for the HRV. In step 1104, the analyticssystem may generate and display intensity score for an entire day or anexercise routine. In some cases, the analytics system may displayquantitative and/or qualitative information corresponding to theintensity score. FIG. 12 illustrates an exemplary display of anintensity score index indicated in a circular graphic component with anexemplary current score of 19.0 indicated. The graphic component mayindicate a degree of difficulty of the exercise corresponding to thecurrent score selected from, for example, maximum all out, near maximal,very hard, hard, moderate, light, active, light active, no activity,asleep, and the like. The display may indicate, for example, that theintensity score corresponds to a good and tapering exercise routine,that the user did not overcome his anaerobic threshold and that the userwill have little to no soreness the next day.

In step 1106, in an exemplary embodiment, the analytics system mayautomatically generate or adjust an exercise routine or regimen based onthe user's actual intensity scores or desired intensity scores. Forexample, based on inputs of the user's actual intensity scores, adesired intensity score (that is higher than the actual intensityscores) and a first exercise routine currently performed by the user(e.g., walking), the analytics system may recommend a second differentexercise routine that is typically associated with higher intensityscores than the first exercise routine (e.g., running).

In step 1108, at any given time during the day (e.g., every morning),the analytics system may generate and display a recovery score. In somecases, the analytics system may display quantitative and/or qualitativeinformation corresponding to the intensity score. For example, in step1110, in an exemplary embodiment, the analytics system may determine ifthe recovery is greater than (or equal to or greater than) a firstpredetermined threshold (e.g., about 60% to about 80% in some examples)that indicates that the user is recovered and is ready for exercise. Ifthis is the case, in step 1112, the analytics system may indicate thatthe user is ready to perform an exercise routine at a desired intensityor that the user is ready to perform an exercise routine morechallenging than the past day's routine. Otherwise, in step 1114, theanalytics system may determine if the recovery is lower than (or equalto or lower than) a second predetermined threshold (e.g., about 10% toabout 40% in some examples) that indicates that the user has notrecovered. If this is the case, in step 1116, the analytics system mayindicate that the user should not exercise and should rest for anextended period. The analytics system may, in some cases, the durationof recommended rest. Otherwise, in step 1118, the analytics system mayindicate that the user may exercise according to his/her exerciseregimen while being careful not to overexert him/herself. The thresholdsmay, in some cases, be adjusted based on a desired intensity at whichthe user desires to exercise. For example, the thresholds may beincreased for higher planned intensity scores.

FIG. 13 illustrates an exemplary display of a recovery score indexindicated in a circular graphic component with a first threshold of 66%and a second threshold of 33% indicated. FIGS. 14A-14C illustrate therecovery score graphic component with exemplary recovery scores andqualitative information corresponding to the recovery scores.

Optionally, in an exemplary embodiment, the analytics system mayautomatically generate or adjust an exercise routine or regimen based onthe user's actual recovery scores (e.g., to recommend lighter exercisefor days during which the user has not recovered sufficiently). Thisprocess may also use a combination of the intensity and recovery scores.

The analytics system may, in some embodiments, determine and display theintensity and/or recovery scores of a plurality of users in acomparative manner. This enables users to match exercise routines withothers based on comparisons among their intensity scores.

IV. EXEMPLARY DISPLAYS AND USER INTERFACES

Exemplary embodiments also provide a vibrant and interactive onlinecommunity for displaying and sharing physiological data among users.Exemplary systems have the ability to stream the physiologicalinformation wirelessly, directly or through a mobile device application,to an online website. The website allows users to monitor their ownfitness results, share information with their teammates and coaches,compete with other users, and win status. Both the wearable system andthe website allow a user to provide feedback regarding his day, whichenables recovery and performance ratings. One aspect is directed toproviding an online website for health and fitness monitoring. In someembodiments, the website may be a social networking site. The websitemay allow users, such as young athletes, to monitor their own fitnessresults, share information with their teammates and coaches, competewith other users, and win prizes. A user may include an individual whosehealth or fitness is being monitored, such as an individual wearing abracelet disclosed herein, an athlete, a sports team member, a personaltrainer or a coach. In some embodiments, a user may pick their owntrainer from a list to comment on their performance.

In some embodiments, the website may be configured to provide aninteractive user interface. The website may be configured to displayresults based on analysis on physiological data received from one ormore devices. The website may be configured to provide competitive waysto compare one user to another, and ultimately a more interactiveexperience for the user. For example, in some embodiments, instead ofmerely comparing a user's physiological data and performance relative tothat user's past performances, the user may be allowed to compete withother users and the user's performance may be compared to that of otherusers.

In some embodiments, the website may be a mobile website or a mobileapplication. In some embodiments, the website may be configured tocommunicate data to other websites or applications.

The exemplary website may include a brief and free sign-up processduring which a user may create an account with his/her name, accountname, email, home address, height, weight, age, and a unique codeprovided in his/her wearable physiological measurement system. Theunique code may be provided, for example, on the wearable system itselfor in the packaged kit. Once subscribed, continuous physiological datareceived from the user's system may be retrieved in a real-timecontinuous basis and presented automatically on a webpage associatedwith the user. Additionally, the user can add information to hisprofile, such as, a picture, favorite activities, sports team(s), andthe user may search for teammates/friends on the website for sharinginformation.

FIGS. 15A-18B illustrate an exemplary user interface 1500 for displayingphysiological data specific to a user as rendered on visual displaydevice. The user interface 1500 may take the form of a webpage in someembodiments. One of ordinary skill in the art will recognize that theinformation in FIGS. 15A-18B represent non-limiting illustrativeexamples. The user interface 1500 may include a summary panel 1502including an identification 1504 of the user (e.g., a real or accountname) with, optionally, a picture or photo corresponding to the user.The summary panel 1502 may also display the current intensity score 1506and the current recovery score 1508 of the user. In some embodiments,the summary panel 1502 may display the number of calories burned by theuser 1510 that day and the number of hours of sleep 1512 obtained by theuser the previous night.

The user interface 1500 may also include panels for presentinginformation on the user's workouts—a workout panel 1514 accessible usingtab 1516, day—a day panel 1518 accessible using tab 1520, and sleep—asleep panel 1522 accessible using tab 1524. The same or differentfeedback panels may be associated with the workout, day and sleeppanels. The panels may enable the user to select and customize one ormore informative panels that appear in his/her user interface display.

The workout panel 1514 may present quantitative information on theuser's health and exercise routines, for example, a graph 1530 of theuser's continuous heart rate during the exercise, statistics 1532 on themaximum heart rate, average heart rate, duration of exercise, number ofsteps taken and calories expended, zones 1534 in which the maximum heartrate fell during the exercise, and a graph 1536 of the intensity scoresover a period of time (e.g., seven days).

A feedback panel 1538 associated with the workout panel 1514 may presentinformation on the intensity score and the exercise routines performedby the user during a selected period of time including, but not limitedto, quantitative information, qualitative information, feedback,recommendations on future exercise routines, and the like. The feedbackpanel 1538 may present the intensity score along with a qualitativesummary 1540 of the score indicating, for example, whether the userpushed past his anaerobic threshold for a considerable period of theexercise, whether the exercise is likely to cause muscle pain andsoreness, and the like. Based on analysis of the quantitative healthparameters monitored during the exercise routine, the feedback panel1538 may present one or more tips 1542 on adjusting the exerciseroutine, for example, that the exercise routine started too rapidly andthat the user should warm up for longer. In some cases, upon selectionof the tips sub-panel 1542, a corresponding indicator 1544 may beprovided in the heart rate graph 1530.

Based on analysis of the quantitative health parameters monitored duringthe exercise routine, the feedback panel 1538 may also presentqualitative information 1545 on the user's exercise routine, forexample, comparison of the present day's exercise routine to the user'shistorical exercise data. Such information may indicate, for example,that the user's maximum heart rate for the day's exercise was thehighest ever recorded, that the steps taken by the user that day was thefewest ever recorded, that the user burned a lot of calories and thatmore calories may be burned by lowering the intensity of the exercise,and the like. The feedback panel 1538 may also present cautionaryindicators 1546 to warn the user of future anticipated health events,for example, the likelihood of soreness (e.g., if the intensity score ishigher than a predefined threshold), and the like.

An exemplary analytics system may analyze the information presented inthe workout panel 1514 and determine whether the user performed aspecific exercise routine or activity. As one example, given a smallnumber of steps taken and a high calorie burn and heart rate, the systemmay determine that it is possible the user rode a bicycle that day. Insome cases, the feedback panel 1538 may prompt the user to confirmwhether he/she indeed performed that activity in a user field 1548. Thisuser input may be displayed and/or used to improve an understanding ofthe user's health and exercise routines.

The day panel 1518 may include information on health parameters of theuser during the current day including, but not limited to, the number ofcalories burned and the number of calories taken in 1500 (which may bebased on user input on the foods eaten), a graph 1554 of the day'scontinuous heart rate, statistics 1556 on the resting heart rate andsteps taken by the user that day, a graph 1558 of the calories burnedthat and other days, and the like.

In some cases, an analytics system may analyze the physiological data(e.g., heart rate data) and estimate the durations of sleep, activityand workout during the day. A feedback panel 1562 associated with theday panel 1518 may present these durations 1564. In some cases, thefeedback panel 1562 may display a net number of calories consumed by theuser that day 1566. Based on analysis of the quantitative healthparameters monitored during the exercise routine, the feedback panel1562 may also present qualitative information 1568 on the user'sexercise routine. Such information may indicate, for example, that theuser was stressed at a certain point in the day (e.g., if there was ahigh level of sweat with little activity), that the user's maximum heartrate for the day's exercise was the highest ever recorded, that thesteps taken by the user that day was the fewest ever recorded, that theuser burned a lot of calories and that more calories may be burned bylowering the intensity of the exercise, and the like. The feedback panel1562 may also present cautionary indicators 1570 to warn the user offuture anticipated health events, for example, tachycardia,susceptibility to illness or overtraining (e.g., if the resting heartrate is elevated for a few days), and the like.

An exemplary analytics system may analyze the information presented inthe day panel 1518 and determine whether the user performed a specificexercise routine or activity. As one example, given an elevated heartrate with little activity, the system may determine that it is possiblethe user drank coffee at that point. In some cases, the feedback panel1562 may prompt the user to confirm whether he/she indeed performed thatactivity in a user field 1572. This user input may be displayed and/orused to improve an understanding of the user's health and exerciseroutines.

The sleep panel 1522 may include information on health parameters of theuser during sleep including, but not limited to, an overlaid graph 1573of heart rate and movement during sleep, statistics 1574 on the maximumheart rate, minimum heart rate, number of times the user awoke duringsleep, average movement during sleep, a sleep cycle indicator 1576showing durations spent awake, in light sleep, in deep sleep and in REMsleep, and a sleep duration graph 1578 showing the number of hours sleptover a period of time.

A feedback panel 1580 associated with the sleep panel 1522 may presentinformation on the user's sleep including, but not limited to,quantitative information, qualitative information, feedback,recommendations on future exercise routines, and the like. The feedbackpanel 1580 may present a sleep score and/or a number of hours of sleepalong with a qualitative summary of the score 1582 indicating, forexample, whether the user slept enough, whether the sleep was efficientor inefficient, whether the user moved around and how much during sleep,and the like. Based on analysis of the quantitative health parametersmonitored during sleep, the feedback panel 1580 may present one or moretips 1584 on adjusting sleep, for example, that the woke up a number oftimes during sleep and that user can try to sleep on his side ratherthan on his back.

Based on analysis of the quantitative health parameters monitored duringthe exercise routine, the feedback panel 1580 may also presentqualitative information 1586 on the user's sleep. Such information mayindicate, for example, that the user's maximum heart rate for the day'sexercise was the highest ever recorded during sleep. The feedback panel1580 may also present cautionary indicators 1588 to warn the user offuture anticipated health events, for example, a sign of overtrainingand a recommendation to get more sleep (e.g., if the user awoke manytimes during sleep and/or if the user moved around during sleep.

The user interface 1500 may provide a user input field 1590 for enablingthe user to indicate his/her feelings, e.g., activities performedperceived exertion, energy level, performance. The user interface 1500may also provide a user input field 1592 for enabling the user toindicate other facts about his exercise routine, e.g., comments on whatthe user was doing at a specific point in the exercise routine with alink 1594 to a corresponding point in the heart rate graph 1530. In someembodiments, the user may specify a route and/or location on a map atwhich the exercise routine was performed.

Exemplary embodiments also enable a user to compare his/her quantitativeand/or qualitative physiological data with those of one or moreadditional users. A user may be presented with user selection componentsrepresenting other users who data is available for display. When apointer is hovered over a user selection component (e.g., an iconrepresenting a user), a snapshot of the user's information is presentedin a popup component, and clicking on the user selection component opensup the full user interface displaying the user's information. In somecases, the user selection components include certain user-specific datasurrounding an image representing the user, for example, a graphicelement indicating the user's intensity score. The user selectioncomponents may be provided in a grid as shown or in a linear listing foreasier sorting. The users appearing in the user selection components maybe sorted and/or ranked based on any desired criteria, e.g., intensityscores, who is experiencing soreness, and the like. A user may leavecomments on other users' pages.

Similarly, a user may select privacy settings to indicate which aspectsof his/her own data may be viewed by other users. Because the wearablesystems described herein support truly continuous monitoring, a user maywish to carefully control whether and when data is transmittedwirelessly, stored in a remote data repository, and shared with others.A privacy switch as described herein may be usefully employed to togglebetween various privacy settings or to explicitly select private orrestricted times when no monitoring should occur.

FIGS. 19A and 19B illustrate an exemplary user interface 1900 renderedon a visual display device for displaying physiological data on aplurality of users. In some cases, a user may freely compare the data ofany users whose data is available and accessible, i.e., set to anappropriate privacy level. In some cases, comparative data maycorrespond to a plurality of users who may be grouped together based onany suitable criteria, e.g., members of a gym, military team, and thelike. In some cases, the user may be able to discover other users orcomparable data by searching or performing queries on any desiredparameters, for example, workouts, activities, age groups, locations,intensities, recoveries and the like. For example, a user may perform aquery for “Workouts above a 17 Intensity in Boston for runners my age.”The exemplary user interface may also identify or suggest users withwhom to exchange data based on similar parameters. Data on any number ofusers may be presented and compared including, but not limited to, 2, 3,4, 5, 6, 7, 8, 9, 10, and the like.

In a default option, data from the same time period(s) may be presentedfor all of the users. In some embodiments, time periods for each usermay be selected independently and data from the selected time periodsmay be displayed in a comparative manner on the same user interface,e.g., in one or more overlaid graphs. FIG. 20 illustrates a userinterface 2000 that may be used to independently select time periods ofdata for each of five users so that the data from the selected periodsmay be displayed together. The user interface 2000 includes arepresentation of each user 2002 a-2002 e, optionally an indication ofeach user's intensity score, a calendar component 2004 for selecting thetime periods, and a component 2006 a-2006 e indicating the time periodsselected for each user. In some cases, data from different time periods(but, for example, for the same time duration) for the same user may bepresented on the same user interface for comparative purpose, forexample, to determine training progress.

In FIGS. 19A and 19B, the user interface 1900 may include a summarypanel 1902 including an identification 1904 a-1904 b of the users (e.g.,a real or account name) with, optionally, a picture or photocorresponding to the user. In some cases, the summary panel 1902 mayalso display certain information associated with the users, for example,their intensity scores.

A workout panel 1908 may present quantitative information on the users'health and exercise routines, for example, an overlaid graph 1910 of theusers' continuous heart rate during the exercise, statistics 1912 on theusers' maximum heart rate, average heart rate, duration of exercise,number of steps taken and calories expended, zones 1914 in which theusers' maximum heart rate fell during the exercise, and an overlaidgraph 1916 of the intensity scores over a period of time (e.g., sevendays). A feedback panel 1918 associated with the workout panel 1908 maypresent comparative qualitative information on the users' exerciseroutines including, but not limited to, whether the users were workingout at the same time, which user had a more difficult workout, thecomparative efficiencies of the users, and the like. Similarly, a daypanel and a sleep panel may present comparative information for theselected users.

The analytics system may analyze comparative data among a plurality ofusers and provide rankings of individuals, teams and groups ofindividuals (e.g., employees of a company, members of a gym) based on,for example, average intensity scores. For each user, the analyticssystem may calculate and display percentile rankings of the user withrespect to all of the users in a community in terms of, for example,intensity scores, quality of sleep, and the like.

Exemplary embodiments also provide user interfaces to enable intuitiveand efficient monitoring of a plurality of users by an individual withadministrative powers to view the users' health data. Such anadministrative user may be a physical instructor, trainer or coach whomay use the interface to manage his/her clients' workout regimen.

FIGS. 21A and 21B illustrate an exemplary user interface 2100 viewableby an administrative user, including a selectable and editablerepresentation or listing 2102 of the users (e.g., a trainer's clients)whose health information is available for display. When a mouse ishovered over a user selection component (e.g., an icon representing auser), a snapshot of the user's information is presented in a popupcomponent, and clicking on the user selection component opens up thefull user interface displaying the user's information. In some cases,the user selection components include certain user-specific datasurrounding an image representing the user, for example, a graphicelement indicating the user's intensity score. The user selectioncomponents in the listing 2102 may be provided in a grid as shown or ina linear listing for easier sorting. The users appearing in the listing2102 may be sorted and/or ranked based on any desired criteria, e.g.,intensity scores, who is experiencing soreness, and the like. Selectionof any one user causes the user interface specific to that user to beopened, for example, as shown in FIGS. 15A-18B. The administrative usermay leave messages on the user interfaces of the different users.Selection of more than one user causes a user interface comparing theselected users to be opened, for example, as shown in FIGS. 19A and 19B.

The administrative user interface 2100 may include a listing of users2104 who recently performed exercise routines including the time oftheir last workout and their intensity scores, a listing of users 2106who are off-schedule in their exercise regimen and how many days theyhave not been exercising, a listing of users 2108 who are experiencingsoreness (that may be determined automatically based on intensityscores), a listing of users who are sleep-deprived (that may bedetermined automatically based on sleep data), and the like. The userinterface 2100 may also display a calendar or portion of a calendar 2110indicating training times for different users. The calendar featureenables the administrative user to review exercise schedules over timeand understand how well individuals or teams are meeting goals. Forexample, the administrative user may determine that an individual isundertraining if his intensity for the day was 18 whereas the teamaverage was 14.

In any of the exemplary user interfaces disclosed herein, color codingmay be used to indicate categories of any parameter. For example, in aday panel of a user interface, color coding may be used to indicatewhether a user's day was difficult (e.g., with the color red), tapering(e.g., with the color yellow), or a day off from training (e.g., withthe color blue).

Exemplary embodiments enable selected qualitative and/or quantitativedata from any of the user interfaces disclosed herein to be selected,packaged and exported to an external application, computational deviceor webpage (e.g., a blog) for display, storage and analysis. The datamay be selected based on any desired characteristic including, but notlimited to, gender, age, location, activity, intensity level, and anycombinations thereof. An online blog may be presented to display thedata and allow users to comment on the data.

V. EXEMPLARY COMPUTING DEVICES

Various aspects and functions described herein may be implemented ashardware, software or a combination of hardware and software on one ormore computer systems. Exemplary computer systems that may be usedinclude, but are not limited to, personal computers, embedded computingsystems, network appliances, workstations, mainframes, networkedclients, servers, media servers, application servers, database servers,web servers, virtual servers, and the like. Other examples of computersystems that may be used include, but are not limited to, mobilecomputing devices, such as wearable devices, cellular phones andpersonal digital assistants, and network equipment, such as loadbalancers, routers and switches.

FIG. 22 is a block diagram of an exemplary computing device 2200 thatmay be used in to perform any of the methods provided by exemplaryembodiments. The computing device may be configured as an embeddedsystem in the integrated circuit board(s) of a wearable physiologicalmeasurements system and/or as an external computing device that mayreceive data from a wearable physiological measurement system.

The computing device 2200 includes one or more non-transitorycomputer-readable media for storing one or more computer-executableinstructions or software for implementing exemplary embodiments. Thenon-transitory computer-readable media may include, but are not limitedto, one or more types of hardware memory, non-transitory tangible media(for example, one or more magnetic storage disks, one or more opticaldisks, one or more USB flash drives), and the like. For example, memory2206 included in the computing device 2200 may store computer-readableand computer-executable instructions or software for implementingexemplary embodiments. The computing device 2200 also includes processor2202 and associated core 2204, and optionally, one or more additionalprocessor(s) 2202′ and associated core(s) 2204′ (for example, in thecase of computer systems having multiple processors/cores), forexecuting computer-readable and computer-executable instructions orsoftware stored in the memory 2206 and other programs for controllingsystem hardware. Processor 2202 and processor(s) 2202′ may each be asingle core processor or multiple core (2204 and 2204′) processor.

Virtualization may be employed in the computing device 2200 so thatinfrastructure and resources in the computing device may be shareddynamically. A virtual machine 2214 may be provided to handle a processrunning on multiple processors so that the process appears to be usingonly one computing resource rather than multiple computing resources.Multiple virtual machines may also be used with one processor.

Memory 2206 may include a computer system memory or random accessmemory, such as DRAM, SRAM, EDO RAM, and the like. Memory 2206 mayinclude other types of memory as well, or combinations thereof.

A user may interact with the computing device 2200 through a visualdisplay device 2218, such as a computer monitor, which may display oneor more user interfaces 2220 that may be provided in accordance withexemplary embodiments. The visual display device 2218 may also displayother aspects, elements and/or information or data associated withexemplary embodiments, for example, views of databases, photos, and thelike. The computing device 2200 may include other I/O devices forreceiving input from a user, for example, a keyboard or any suitablemulti-point touch interface 2208, a pointing device 2210 (e.g., amouse). The keyboard 2208 and the pointing device 2210 may be coupled tothe visual display device 2218. The computing device 2200 may includeother suitable conventional I/O peripherals.

The computing device 2200 may also include one or more storage devices2224, such as a hard-drive, CD-ROM, or other computer readable media,for storing data and computer-readable instructions and/or software thatimplement exemplary methods as taught herein. Exemplary storage device2224 may also store one or more databases 2026 for storing any suitableinformation required to implement exemplary embodiments. The databasesmay be updated by a user or automatically at any suitable time to add,delete or update one or more items in the databases.

The computing device 2200 may include a network interface 2212configured to interface via one or more network devices 2222 with one ormore networks, for example, Local Area Network (LAN), Wide Area Network(WAN) or the Internet through a variety of connections including, butnot limited to, standard telephone lines, LAN or WAN links (for example,802.11, T1, T3, 56 kb, X.25), broadband connections (for example, ISDN,Frame Relay, ATM), wireless connections, controller area network (CAN),or some combination of any or all of the above. The network interface2212 may include a built-in network adapter, network interface card,PCMCIA network card, card bus network adapter, wireless network adapter,USB network adapter, modem or any other device suitable for interfacingthe computing device 2200 to any type of network capable ofcommunication and performing the operations described herein. Moreover,the computing device 2200 may be any computer system, such as aworkstation, desktop computer, server, laptop, handheld computer, tabletcomputer (e.g., the iPad® tablet computer), mobile computing orcommunication device (e.g., the iPhone® communication device), or otherform of computing or telecommunications device that is capable ofcommunication and that has sufficient processor power and memorycapacity to perform the operations described herein.

The wearable physiological measurement system may record and transmit atleast the following types of data to an external computing system,mobile communication system or the Internet: raw continuously-detecteddata (e.g., heart rate data, movement data, galvanic skin response data)and processed data based on the raw data (e.g., RR intervals determinedfrom the heart rate data). Transmission modes may be wired (e.g., usingUSB stick inserted into a USB port on the system) or wireless (e.g.,using a wireless transmitter). The raw and processed data may betransmitted together or separately using different transmission modes.Since a raw data file is typically substantially larger than a processeddata file, the raw data file may be transmitted using WiFi or a USBstick, while the processed data file may be transmitted using Bluetooth.

An exemplary wearable system may include a 2G, 3G or 4G chip thatwirelessly uploads all data to the website disclosed herein withoutrequiring any other external device. A 3G or 4G chip may be usedpreferably as a 2G connection on a Nokia 5800 was found to transfer dataat a rate of 520 kbps using 1.69 W, while a 3G connection transferred at960 kbps using 1.73 W. Therefore, the 3G chip would use negligibly morepower for almost twice the transfer speed, thereby halving half thetransfer time and using much less energy from the battery.

In some cases, the wearable system may opportunistically transfer datawhen in close proximity to a streaming outlet. For example, the systemmay avoid data transmission when it is not within close proximity of astreaming outlet, and, when nearby a streaming outlet (e.g., a linkedphone), may send the data to the external device via Bluetooth and tothe Internet via the external device. This is both convenient and “free”in the sense that it utilizes existing cellular data plans.

Limiting the frequency with which data is streamed increases thewearable system's battery life. In one non-limiting example, the systemmay be set to stream automatically in the morning and following a timestamp. Regardless of the data transmission scheme, the system stores allthe data it collects. Data may also be streamed on demand by a user, forexample, by turning a physical component on the system and holding it orby initiating a process on the mobile application or receiving device.In some embodiments, the data transmission frequency may beautomatically adjusted based on one or more physiological parameters,e.g., heart rate. For example, higher heart rates may prompt morefrequent and real-time streaming transmission of data.

The computing device 2200 may run any operating system 2216, such as anyof the versions of the Microsoft® Windows® operating systems, thedifferent releases of the Unix and Linux operating systems, any versionof the MacOS® for Macintosh computers, any embedded operating system,any real-time operating system, any open source operating system, anyproprietary operating system, any operating systems for mobile computingdevices, or any other operating system capable of running on thecomputing device and performing the operations described herein. Inexemplary embodiments, the operating system 2216 may be run in nativemode or emulated mode. In an exemplary embodiment, the operating system2216 may be run on one or more cloud machine instances.

VI. EXEMPLARY NETWORK ENVIRONMENTS

Various aspects and functions of the implementations may be distributedamong one or more computer systems configured to provide a service toone or more client computers, or to perform an overall task as part of adistributed system. Additionally, aspects may be performed on aclient-server or multi-tier system that includes components distributedamong one or more server systems that perform various functions. Thus,the implementations are not limited to executing on any particularsystem or group of systems. Further, aspects may be implemented insoftware, hardware or firmware, or any combination thereof. Thus,aspects may be implemented within methods, acts, systems, systemplacements and components using a variety of hardware and softwareconfigurations, and they are not limited to any particular distributedarchitecture, network or communication protocol. Furthermore, aspectsmay be implemented as specially-programmed hardware and/or software.

FIG. 23 is a block diagram of an exemplary distributed computer system2300 in which various aspects and functions may be practiced. Thedistributed computer system 2300 may include one or more computersystems. For example, as illustrated, the distributed computer system2300 includes three computer systems 2302, 2304 and 2306. As shown, thecomputer systems 2302, 2304, 2306 are interconnected by, and mayexchange data through, a communication network 2308. The network 2308may include any communication network through which computer systems mayexchange data. To exchange data via the network 2308, the computersystems and the network may use various methods, protocols and standardsincluding, but not limited to, token ring, Ethernet, wireless Ethernet,Bluetooth, TCP/IP, UDP, HTTP, FTP, SNMP, SMS, MMS, SS7, JSON, XML, REST,SOAP, CORBA, HOP, RMI, DCOM and Web Services. To ensure data transfer issecure, the computer systems may transmit data via the network using avariety of security measures including, but not limited to, TSL, SSL andVPN. While the distributed computer system 2300 illustrates threenetworked computer systems, the distributed computer system may includeany number of computer systems, networked using any medium andcommunication protocol.

Various aspects and functions may be implemented as specialized hardwareor software executing in one or more computer systems. As depicted, thecomputer system 2300 includes a processor 2310, a memory 2312, a bus2314, an interface 2316 and a storage system 2318. The processor 2310,which may include one or more microprocessors or other types ofcontrollers, can perform a series of instructions that manipulate data.The processor 2310 may be a well-known commercially-available processorsuch as an Intel Pentium, Intel Atom, ARM Processor, Motorola PowerPC,SGI MIPS, Sun UltraSPARC or Hewlett-Packard PA-RISC processor, or may beany other type of processor or controller as many other processors andcontrollers are available. The processor 2310 may be a mobile device orsmart phone processor, such as an ARM Cortex processor, a QualcommSnapdragon processor or an Apple processor. As shown, the processor 2310is connected to other system placements, including a memory 2312, by thebus 2314.

The memory 2312 may be used for storing programs and data duringoperation of the computer system 2300. Thus, the memory 2312 may be arelatively high performance, volatile, random access memory such as adynamic random access memory (DRAM) or static memory (SRAM). However,the memory 2312 may include any device for storing data, such a diskdrive or other non-volatile storage device, such as flash memory orphase-change memory (PCM). Various embodiments can organize the memory2312 into particularized and, in some cases, unique structures toperform the aspects and functions disclosed herein.

Components of the computer system 2300 may be coupled by aninterconnection element such as the bus 2314. The bus 2314 may includeone or more physical busses (for example, buses between components thatare integrated within the same machine) and may include anycommunication coupling between system placements including specializedor standard computing bus technologies such as IDE, SCSI, PCI andInfiniBand. Thus, the bus 2314 enables communications (for example, dataand instructions) to be exchanged between system components of thecomputer system 2300.

Computer system 2300 also includes one or more interface devices 2316,such as input devices, output devices and combination input/outputdevices. The interface devices 2316 may receive input, provide output,or both. For example, output devices may render information for externalpresentation. Input devices may accept information from externalsources. Examples of interface devices include, but are not limited to,keyboards, mouse devices, trackballs, microphones, touch screens,printing devices, display screens, speakers, network interface cards,and the like. The interface devices 2316 allow the computer system 2300to exchange information and communicate with external entities, such asusers and other systems.

Storage system 2318 may include one or more computer-readable andcomputer-writeable non-volatile and non-transitory storage media onwhich computer-executable instructions are encoded that define a programto be executed by the processor. The storage system 2318 also mayinclude information that is recorded on or in the media, and thisinformation may be processed by the program. More specifically, theinformation may be stored in one or more data structures specificallyconfigured to conserve storage space or increase data exchangeperformance. The instructions may be persistently stored as encodedsignals, and the instructions may cause a processor to perform any ofthe functions described herein. A medium that can be used with variousembodiments may include, for example, optical disk, magnetic disk orflash memory, among others. In operation, the processor 2310 or someother controller may cause data to be read from the non-transitoryrecording media into another memory, such as the memory 2312, thatallows for faster access to the information by the processor than doesthe storage medium included in the storage system 2318. The memory maybe located in the storage system 2318 and/or in the memory 2312. Theprocessor 2310 may manipulate the data within the memory 2312, and thencopy the data to the medium associated with the storage system 2318after processing is completed. A variety of components may manage datamovement between the media and the memory 2312, and the presentdisclosure is not limited thereto.

Further, the implementations are not limited to a particular memorysystem or storage system. Although the computer system 2300 is shown byway of example as one type of computer system upon which various aspectsand functions may be practiced, aspects are not limited to beingimplemented on the computer system. Various aspects and functions may bepracticed on one or more computers having different architectures orcomponents than that shown in the illustrative figures. For instance,the computer system 2300 may include specially-programmed,special-purpose hardware, such as for example, an application-specificintegrated circuit (ASIC) tailored to perform a particular operationdisclosed herein. Another embodiment may perform the same function usingseveral general-purpose computing devices running MAC OS® System X withMotorola PowerPC® processors and several specialized computing devicesrunning proprietary hardware and operating systems.

The computer system 2300 may include an operating system that manages atleast a portion of the hardware placements included in computer system2300. A processor or controller, such as processor 2310, may execute anoperating system which may be, among others, a Windows-based operatingsystem (for example, Windows NT, Windows 2000/ME, Windows XP, Windows 7,or Windows Vista) available from the Microsoft Corporation, a MAC OS®System X operating system available from Apple Computer, one of manyLinux-based operating system distributions (for example, the EnterpriseLinux operating system available from Red Hat Inc.), a Solaris operatingsystem available from Sun Microsystems, or a UNIX operating systemsavailable from various sources. The operating system may be a mobiledevice or smart phone operating system, such as Windows Mobile, Androidor iOS. Many other operating systems may be used, and embodiments arenot limited to any particular operating system.

The processor and operating system together define a computing platformfor which application programs in high-level programming languages maybe written. These component applications may be executable, intermediate(for example, C# or JAVA bytecode) or interpreted code which communicateover a communication network (for example, the Internet) using acommunication protocol (for example, TCP/IP). Similarly, functions maybe implemented using an object-oriented programming language, such asSmallTalk, JAVA, C++, Ada, or C# (C-Sharp). Other object-orientedprogramming languages may also be used. Alternatively, procedural,scripting, or logical programming languages may be used.

Additionally, various functions may be implemented in a non-programmedenvironment (for example, documents created in HTML, XML or other formatthat, when viewed in a window of a browser program, render aspects of agraphical-user interface or perform other functions). Further, variousembodiments may be implemented as programmed or non-programmedplacements, or any combination thereof. For example, a web page may beimplemented using HTML while a data object called from within the webpage may be written in C++. Thus, the implementations are not limited toa specific programming language and any suitable programming languagecould also be used.

A computer system included within an embodiment may perform functionsoutside the scope of the embodiment. For instance, aspects of the systemmay be implemented using an existing product. Aspects of the system maybe implemented on database management systems such as SQL Serveravailable from Microsoft of Seattle, Washington; Oracle Database fromOracle of Redwood Shores, Calif.; and MySQL from Sun Microsystems ofSanta Clara, Calif.; or integration software such as WebSpheremiddleware from IBM of Armonk, N.Y. However, a computer system running,for example, SQL Server may be able to support both aspects in accordwith the implementations and databases for sundry applications notwithin the scope of the disclosure.

FIG. 24 is a diagram of an exemplary network environment 2400 suitablefor a distributed implementation of exemplary embodiments. The networkenvironment 2400 may include one or more servers 2402 and 2404 coupledto one or more clients 2406 and 2408 via a communication network 2410.The network interface 2212 and the network device 2222 of the computingdevice 2200 enable the servers 2402 and 2404 to communicate with theclients 2406 and 2408 via the communication network 2410. Thecommunication network 2410 may include, but is not limited to, theInternet, an intranet, a LAN (Local Area Network), a WAN (Wide AreaNetwork), a MAN (Metropolitan Area Network), a wireless network, anoptical network, and the like. The communication facilities provided bythe communication network 2410 are capable of supporting distributedimplementations of exemplary embodiments.

In an exemplary embodiment, the servers 2402 and 2404 may provide theclients 2406 and 2408 with computer-readable and/or computer-executablecomponents or products under a particular condition, such as a licenseagreement. For example, the computer-readable and/or computer-executablecomponents or products may include those for providing and rendering anyof the user interfaces disclosed herein. The clients 2406 and 2408 mayprovide and render an exemplary graphical user interface using thecomputer-readable and/or computer-executable components and productsprovided by the servers 2402 and 2404.

Alternatively, in another exemplary embodiment, the clients 2406 and2408 may provide the servers 2402 and 2404 with computer-readable andcomputer-executable components or products under a particular condition,such as a license agreement. For example, in an exemplary embodiment,the servers 2402 and 2404 may provide and render an exemplary graphicaluser interface using the computer-readable and/or computer-executablecomponents and products provided by the clients 2406 and 2408.

FIG. 25 is a flow chart of a method 2500 according to an implementation.

As shown in step 2502, the method 2500 may include providing a strapwith a sensor and a heart rate monitoring system. The strap may beshaped and sized to fit about an appendage. For example, the strap maybe any of the straps described herein, including, without limitation, abracelet. The heart rate monitoring system may be configured to providetwo or more different modes for detecting a heart rate of a wearer ofthe strap. The modes may include the use of optical detectors (e.g.,light detectors), light emitters, motion sensors, a processing module,algorithms, other sensors, a peak detection technique, a frequencydomain technique, variable optical characteristics, non-opticaltechniques, and so on.

As shown in step 2504, the method 2500 may include detecting a signalfrom the sensor. The signal may be detected by one or more sensors,which may include any of the sensors described herein. The signal mayinclude, without limitation, one or more signals associated with theheart rate of the user, other physiological signals, an optical signal,signals based on movement, signals based on environmental factors,status signals (e.g., battery life), historical information, and so on.

As shown in step 2506, the method 2500 may include determining acondition of the heart rate monitoring system, which may be based uponthe signal. The condition may include, without limitation, an accuracyof heart rate detection determined using a statistical analysis toprovide a confidence level in the accuracy, a power consumption, abattery charge level, a user activity, a location of the sensor ormotion of the sensor, an environmental or contextual condition (e.g.,ambient light conditions), a physiological condition, an activecondition, an inactive condition, and so on. This may include detectinga change in the condition, responsively selecting a different one of thetwo or more different modes, and storing additional continuous heartrate data obtained using at least one of the two or more differentmodes.

As shown in step 2508, the method 2500 may include selecting one of thetwo or more different modes for detecting the heart rate based on thecondition. For example, based on the motion status of the user, themethod may automatically and selectively activate one or more lightemitters to determine a heart rate of the user. The system may also orinstead determine the type of sensor to use at a given time based on thelevel of motion, skin temperature, heart rate, and the like. Based on acombination of these factors the system may selectively choose whichtype of sensor to use in monitoring the heart rate of the user. Aprocessor or the like may be configured to select one of the modes. Forexample, if the condition is the accuracy of heart rate detectiondetermined using a statistical analysis to provide a confidence level inthe accuracy, the processor may be configured to select a different oneof the modes when the confidence level is below a predeterminedthreshold.

As shown in step 2510, the method 2500 may include storing continuousheart rate data using one of the two or more different modes. This mayinclude communicating the continuous heart rate data from the strap to aremote data repository. This may also or instead including storing thedata locally, e.g., on a memory included on the strap. The memory may beremovable, e.g., via a data card or the like, or the memory may bepermanently attached/integral with the strap or a component thereof. Thestored data (e.g., heart rate data) may be for the user's private use,for example, when in a private setting, or the data may be shared whenin a shared setting (e.g., on a social networking site or the like). Themethod 2500 may further include the use of a privacy switch operable bythe user to controllably restrict communication of a portion of thedata, e.g., to the remote data repository.

FIG. 26 is a flow chart of a method 2600 according to an implementation.

As shown in step 2602, the method 2600 may include monitoring data froma wearable system. The wearable system may be a continuous-monitoring,physiological measurement system worn by a user. The data may includeheart rate data, other physiological data, summary data, motion data,fitness data, activity data, or any other data described herein orotherwise contemplated by a skilled artisan.

As shown in step 2604, the method 2600 may include detecting exerciseactivity. This may include automatically detecting exercise activity ofthe user. The exercise activity may be detected through the use of oneor more sensors as described herein. The exercise activity may be sentto a server that, e.g., performs step 2606 described below.

As shown in step 2606, the method 2600 may include generating anassessment of the exercise activity. This may include generating aquantitative assessment of the exercise activity. Generating aquantitative assessment of the exercise activity may include analyzingthe exercise activity on a remote server. Generating a quantitativeassessment may include the use of the algorithms discussed herein. Themethod 2600 may also include generating periodic updates to the userconcerning the exercise activity. The method 2600 may also includedetermining a qualitative assessment of the exercise activity andcommunicating the qualitative assessment to the user.

As shown in step 2608, the method 2600 may include detecting a recoverystate. This may include automatically detecting a physical recoverystate of the user. The recovery state may be detected through the use ofone or more sensors as described herein. The recovery state may be sentto a server that, e.g., performs step 2610 described below.

As shown in step 2610, the method 2600 may include generating anassessment of the recovery state. This may include generating aquantitative assessment of the physical recovery state. Generating aquantitative assessment may include the use of the algorithms discussedherein. Generating a quantitative assessment of the physical recoverystate may include analyzing the physical recovery state on a remoteserver. The method 2600 may also include generating periodic updates tothe user concerning the physical recover state. The method 2600 may alsoinclude determining a qualitative assessment of the recovery state andcommunicating the qualitative assessment to the user.

As shown in step 2612, the method 2600 may include analyzing theassessments, i.e., analyzing the quantitative assessment of the exerciseactivity and the quantitative assessment of the physical recovery. Theanalysis may include the use of one or more of the algorithms describedherein, a statistical analysis, and so on. The analysis may include theuse of a remote server.

As shown in step 2614, the method 2600 may include generating arecommendation. This may include automatically generating arecommendation on a change to an exercise routine of the user based onthe analysis performed in step 2612. This may also or instead includedetermining a qualitative assessment of the exercise activity and/orrecovery state, and communicating the qualitative assessment(s) to theuser. The recommendation may be generated on a remote server. Therecommendation may be communicated to the user in an electronic mail, itmay be presented to the user in a web page, other communicationsinterface, or the like. Generating the recommendation may be based upona number of cycles of exercise and rest.

The method 2600 described above, or any of the methods discussed herein,may also or instead be implemented on a computer program productincluding non-transitory computer executable code embodied in anon-transitory computer-readable medium that executes on one or morecomputing devices to perform the method steps. For example, code may beprovided that performs the various steps of the methods describedherein.

VII. EQUIVALENTS

It is to be appreciated that embodiments of the systems, apparatuses andmethods discussed herein are not limited in application to the detailsof construction and the arrangement of components set forth in thefollowing description or illustrated in the accompanying drawings.Exemplary systems, apparatuses and methods are capable of implementationin other embodiments and of being practiced or of being carried out invarious ways. Examples of specific implementations are provided hereinfor illustrative purposes only and are not intended to be limiting. Inparticular, acts, elements and features discussed in connection with anyone or more embodiments are not intended to be excluded from a similarrole in any other embodiments. One or more aspects and embodimentsdisclosed herein may be implemented on one or more computer systemscoupled by a network (e.g., the Internet).

The phraseology and terminology used herein are for the purpose ofdescription and should not be regarded as limiting. Any references toembodiments or elements or acts of the systems and methods hereinreferred to in the singular may also embrace embodiments including aplurality of these elements, and any references in plural to anyembodiment or element or act herein may also embrace embodimentsincluding only a single element. The use herein of terms like“including,” “comprising,” “having,” “containing,” “involving,” andvariations thereof, is meant to encompass the items listed thereafterand equivalents thereof as well as additional items. Any referencesfront and back, left and right, top and bottom, upper and lower, andvertical and horizontal, are intended for convenience of description,not to limit the present systems and methods or their components to anyone positional or spatial orientation.

In describing exemplary embodiments, specific terminology is used forthe sake of clarity. For purposes of description, each specific term isintended to, at least, include all technical and functional equivalentsthat operate in a similar manner to accomplish a similar purpose.Additionally, in some instances where a particular exemplary embodimentincludes a plurality of system elements or method steps, those elementsor steps may be replaced with a single element or step. Likewise, asingle element or step may be replaced with a plurality of elements orsteps that serve the same purpose. Further, where parameters for variousproperties are specified herein for exemplary embodiments, thoseparameters may be adjusted up or down by 1/20th, 1/10th, ⅕th, ⅓rd, ½nd,and the like, or by rounded-off approximations thereof, unless otherwisespecified. Moreover, while exemplary embodiments have been shown anddescribed with references to particular embodiments thereof, those ofordinary skill in the art will understand that various substitutions andalterations in form and details may be made therein without departingfrom the scope of the disclosure. Further still, other aspects,functions and advantages are also within the scope of the disclosure.

Embodiments disclosed herein may be combined with other embodimentsdisclosed herein in any manner consistent with at least one of theprinciples disclosed herein, and references to “an embodiment,” “oneembodiment,” “an exemplary embodiment,” “some embodiments,” “someexemplary embodiments,” “an alternate embodiment,” “variousembodiments,” “exemplary embodiments,” and the like, are not necessarilymutually exclusive and are intended to indicate that a particularfeature, structure, characteristic or functionality described may beincluded in at least one embodiment. The appearances of such termsherein are not necessarily all referring to the same embodiment.

Exemplary flowcharts are provided herein for illustrative purposes andare non-limiting examples of methods. One of ordinary skill in the artwill recognize that exemplary methods may include more or fewer stepsthan those illustrated in the exemplary flowcharts, and that the stepsin the exemplary flowcharts may be performed in a different order thanthe order shown in the illustrative flowcharts.

The above systems, devices, methods, processes, and the like may berealized in hardware, software, or any combination of these suitable forthe control, data acquisition, and data processing described herein.This includes realization in one or more microprocessors,microcontrollers, embedded microcontrollers, programmable digital signalprocessors or other programmable devices or processing circuitry, alongwith internal and/or external memory. This may also, or instead, includeone or more application specific integrated circuits, programmable gatearrays, programmable array logic components, or any other device ordevices that may be configured to process electronic signals. It willfurther be appreciated that a realization of the processes or devicesdescribed above may include computer-executable code created using astructured programming language such as C, an object orientedprogramming language such as C++, or any other high-level or low-levelprogramming language (including assembly languages, hardware descriptionlanguages, and database programming languages and technologies) that maybe stored, compiled or interpreted to run on one of the above devices,as well as heterogeneous combinations of processors, processorarchitectures, or combinations of different hardware and software.

Thus, in one aspect, each method described above and combinationsthereof may be embodied in computer executable code that, when executingon one or more computing devices, performs the steps thereof. In anotheraspect, the methods may be embodied in systems that perform the stepsthereof, and may be distributed across devices in a number of ways, orall of the functionality may be integrated into a dedicated, standalonedevice or other hardware. The code may be stored in a non-transitoryfashion in a computer memory, which may be a memory from which theprogram executes (such as random access memory associated with aprocessor), or a storage device such as a disk drive, flash memory orany other optical, electromagnetic, magnetic, infrared or other deviceor combination of devices. In another aspect, any of the systems andmethods described above may be embodied in any suitable transmission orpropagation medium carrying computer-executable code and/or any inputsor outputs from same. In another aspect, means for performing the stepsassociated with the processes described above may include any of thehardware and/or software described above. All such permutations andcombinations are intended to fall within the scope of the presentdisclosure.

It should further be appreciated that the methods above are provided byway of example. Absent an explicit indication to the contrary, thedisclosed steps may be modified, supplemented, omitted, and/orre-ordered without departing from the scope of this disclosure.

The method steps of the invention(s) described herein are intended toinclude any suitable method of causing such method steps to beperformed, consistent with the patentability of the following claims,unless a different meaning is expressly provided or otherwise clear fromthe context. So for example performing the step of X includes anysuitable method for causing another party such as a remote user, aremote processing resource (e.g., a server or cloud computer) or amachine to perform the step of X. Similarly, performing steps X, Y and Zmay include any method of directing or controlling any combination ofsuch other individuals or resources to perform steps X, Y and Z toobtain the benefit of such steps. Thus method steps of theimplementations described herein are intended to include any suitablemethod of causing one or more other parties or entities to perform thesteps, consistent with the patentability of the following claims, unlessa different meaning is expressly provided or otherwise clear from thecontext. Such parties or entities need not be under the direction orcontrol of any other party or entity, and need not be located within aparticular jurisdiction.

It will be appreciated that the methods and systems described above areset forth by way of example and not of limitation. Numerous variations,additions, omissions, and other modifications will be apparent to one ofordinary skill in the art. In addition, the order or presentation ofmethod steps in the description and drawings above is not intended torequire this order of performing the recited steps unless a particularorder is expressly required or otherwise clear from the context. Thus,while particular embodiments have been shown and described, it will beapparent to those skilled in the art that various changes andmodifications in form and details may be made therein without departingfrom the spirit and scope of this disclosure and are intended to form apart of the invention as defined by the following claims, which are tobe interpreted in the broadest sense allowable by law.

What is claimed is:
 1. A wearable physiological measurement systemcomprising: a wearable strap configured to be couplable to an appendageof a user, the wearable strap comprising: one or more light emitters foremitting light toward the user; one or more light detectors forreceiving at least a portion of the light reflected from the user; amotion sensor; and a processing module configured to analyze datacorresponding to the light reflected from the user to automatically andcontinually determine a heart rate of the user, wherein the processingmodule is configured by computer executable code stored in a memory todetermine the heart rate by: applying a peak detection algorithm todetect a plurality of peaks in the data associated with a plurality ofheart beats of the user, determining an R-wave-to-R-wave interval (an RRinterval) based on the plurality of peaks detected by the peak detectionalgorithm, determining a confidence level associated with the RRinterval, wherein the confidence level is based at least in part on amotion signal from the motion sensor, and based on the confidence levelassociated with the RR interval, selecting either the peak detectionalgorithm or a frequency analysis algorithm to calculate the heart rateof the user based on data.
 2. The wearable physiological measurementsystem of claim 1, wherein: in response to determining that theconfidence level associated with the RR interval is above apredetermined threshold, the processing module uses the plurality ofpeaks to determine the heart rate of the user; and in response todetermining that the confidence level associated with the RR interval isbelow the predetermined threshold, the processing module uses thefrequency analysis algorithm to determine the heart rate of the user. 3.A system comprising: a wearable physiological monitor including one ormore optical detectors configured to provide an optical signalindicative of reflected light from a skin of a wearer; a memory storing;and one or more processors configured by computer-readable instructionsstored in the memory to perform the steps of: evaluating an accuracy ofa heart rate signal derived from the optical signal, in response todetermining that the accuracy of the heart rate signal meets apredetermined threshold, calculating a heart rate of the wearer using atime domain peak detection algorithm to evaluate the heart rate signal,and in response to determining that the accuracy of the heart ratesignal does not meet the predetermined threshold, calculating the heartrate of the wearer using a frequency domain algorithm to evaluate theheart rate signal.
 4. The system of claim 3, wherein evaluating theaccuracy includes using a statistical analysis to provide a confidencelevel in the heart rate signal.
 5. The system of claim 3, wherein thewearable physiological monitor includes a motion sensor, and whereinevaluating the accuracy includes evaluating the accuracy based at leastin part on a motion signal from the motion sensor.
 6. The system ofclaim 3, further comprising a sensor coupled to the wearablephysiological monitor, wherein evaluating the accuracy includesevaluating the accuracy based at least in part on a signal from thesensor.
 7. The system of claim 6, wherein the sensor includes one ormore of a position sensor, a timer, a temperature sensor, a GalvanicSkin Response sensor, and a humidity sensor.
 8. The system of claim 3,further comprising a motion sensor, wherein the one or more processorsare configured to process the heart rate signal based on a motion signalfrom the motion sensor to reduce contributions to the heart rate signalby a motion of the wearer.
 9. The system of claim 3, wherein thewearable physiological monitor includes a motion sensor, and wherein thefrequency domain algorithm accounts for a motion of the wearer based ona motion signal from the motion sensor.
 10. The system of claim 3,wherein the wearable physiological monitor includes a wearable strapconfigured to couple the wearable physiological monitor to an appendageof the wearer.
 11. The system of claim 3, further comprising anaccessory removably and replaceably couplable to the wearablephysiological monitor, the accessory including a power source configuredto charge a battery of the wearable physiological monitor without awired coupling to other power sources.
 12. The system of claim 3,wherein the one or more processors include at least one processor in thewearable physiological monitor.
 13. The system of claim 3, wherein theone or more processors include at least one processor on a servercoupled in a communicating relationship with the wearable physiologicalmonitor.
 14. The system of claim 3, wherein the one or more processorsinclude at least one processor on a mobile computing device associatedwith the wearer.
 15. The system of claim 3, wherein the one or moreprocessors are further configured to determine a heart rate variabilityof the wearer based on a sequence of measurements of an instantaneousheart rate of the wearer derived from the heart rate signal.
 16. Acomputer program product comprising non-transitory computer executablecode embodied in a non-transitory computer readable medium that, whenexecuting on one or more computing devices, performs the steps of:receiving an optical signal from a wearable physiological monitor, theoptical signal indicative of reflected light from a skin of a wearer ofthe wearable physiological monitor and detected by an optical detectorof the wearable physiological monitor; evaluating an accuracy of a heartrate signal derived from the optical signal; in response to determiningthat the accuracy is above a predetermined threshold, calculating aheart rate of the wearer using a time domain peak detection algorithmfor heart rate detection; and in response to determining that theaccuracy is below the predetermined threshold, calculating the heartrate of the wearer using a frequency domain algorithm for heart ratedetection.
 17. The computer program product of claim 16, whereinevaluating the accuracy of the heart rate signal includes evaluating theaccuracy based on a motion of the wearer.
 18. The computer programproduct of claim 17, wherein the motion is detected based on a motionsignal from one or more accelerometers associated with the wearablephysiological monitor.
 19. The computer program product of claim 16,wherein evaluating the accuracy includes using a statistical analysis ofthe heart rate signal to determine a statistical confidence level in theaccuracy.
 20. The computer program product of claim 16, furthercomprising code that performs the step of determining a heart ratevariability of the wearer based on a sequence of measurements of aninstantaneous heart rate of the wearer based on the heart rate signal.